Reviewerly/exHarmony huggingface dataset:
Analysis of Computer Information Processing Technology Based on Unstructured Data

The latest research results of complex network and information processing provide a new method and perspective for unstructured and semi-structured data mining. Information service system is centered on information content, which is represented by a large number of various documents and data. In the information service system without database, only unstructured data exists. Most information resources are no longer just structured data, but provided to users in various unstructured forms. In order to dig up the data wealth, it is necessary to create an efficient, convenient and safe storage platform with large capacity, so as to store these huge data effectively. In the unstructured information processing platform, users and resources are often in dynamic changes, and users may need to be dynamically authorized in different collaborative stages. This paper will take unstructured data as the object, study the related technologies of information processing, and integrate it into the complex network construction, and study how to build multi-type networks to make full use of unstructured data.
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level

Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spatial attention networks are known to learn this relationship and focus its gaze on salient objects in the image. Thus, we propose to utilize spatial attention networks for image-level visual-textual fusion preserving local (word) and global (phrase) information to refine region proposals with an in-network Region Proposal Network (RPN) and detect single or multiple regions for a phrase query. We focus only on the phrase query - ground truth pair (referring expression) for a model independent of the constraints of the datasets i.e. additional attributes, context etc. For such referring expression dataset ReferIt game, our Multi-region Attention-assisted Grounding network (MAGNet) achieves over 12% improvement over the state-of-the-art. Without the context from image captions and attribute information in Flickr30k Entities, we still achieve competitive results compared to the state-of-the-art.
Recurrent Graph Convolutional Networks for Skeleton-based Action Recognition

Human action recognition is one of the challenging and active research fields due to its wide range of applications. Recently, graph convolutions for skeleton-based action recognition have attracted much attention. Generally, the adjacency matrices of the graph are fixed to the hand-crafted physical connectivity of the human joints, or learned adaptively via deep learning. The hand-crafted or learned adjacency matrices are fixed when processing each frame of an action sequence. However, the interactions of different subsets of joints may play a core role at different phases of an action. Therefore, it is reasonable to evolve the graph topology with time. In this paper, a recurrent graph convolution is proposed, in which the graph topology is evolved via a long short-term memory (LSTM) network. The proposed recurrent graph convolutional network (R-GCN) can recurrently learn the data-dependent graph topologies for different layers, different time steps and different kinds of actions. Experimental results on the NTU RGB+D and Kinetics-Skeleton datasets demonstrate the advantages of the proposed R-GCN.
Association Rule Mining by Discretization of Agricultural Data Using Extended Partitioning Algorithm

The agricultural dataset is a group of interrelated, discrete items of data that may be used for numerous data groups such as rural development project data, soil data, production advice and productivity data, land use data, pest and disease management data, etc. Aggregation of multidimensional attributes of agricultural data for mining quantitative association rules requires additional scans of large databases. It reduces the performance of the Partitioning Algorithm (PA) used for mining quantitative association rules. Extended Partitioning Algorithm (EPA) has been implemented in the present paper to reduce the number of additional scans for aggregation of multidimensional attributes at different levels. The number of association rules mined from EPA has been compared with the number of association rules mined from PA.
You’ve Left Me No Choices: Security Economics to Inform Behaviour Intervention Support in Organizations

Security policy-makers (influencers) in an organization set security policies that embody intended behaviours for employees (as decision-makers) to follow. Decision-makers then face choices, where this is not simply a binary decision of whether to comply or not, but also how to approach compliance and secure working alongside other workplace pressures, and limited resources for identifying optimal security-related choices. Conflict arises due to information asymmetries present in the relationship, where influencers and decision-makers both consider costs, gains, and losses in ways which are not necessarily aligned. With the need to promote ‘good enough’ decisions about security-related behaviours under such constraints, we hypothesize that actions to resolve this misalignment can benefit from constructs from both traditional economics and behavioural economics. Here we demonstrate how current approaches to security behaviour provisioning in organizations mirror rational-agent economics, even where behavioural economics is embodied in the promotion of individual security behaviours. We develop and present a framework to accommodate bounded security decision-making, within an ongoing programme of behaviours which must be provisioned for and supported. We also point to applications of the framework in negotiating sustainable security behaviours, such as policy concordance and just security cultures.
Reducing False Positives in Object Tracking with Siamese Network

We propose a robust long-term object tracking method that resolves the fundamental cause of the drift and loss of a target in visual object tracking. The proposed method consists of “sampling area extension”, which prevents a tracking result from drifting to other objects by learning false positive samples in advance (before they enter the search region of the target), and “adaptive search based on motion models”, which prevents a tracking result from drifting to other objects and avoids the loss of the target by using not only appearance features but also motion models to adaptively search for the target. Experiments conducted on long-term tracking dataset showed that our first technique improved robustness by 16.6% while the second technique improved robustness by 15.3%. By combining both, our method achieved 21.7% and 9.1% improvement for the robustness and precision, and the processing speed became 3.3 times faster. Additional experiments showed that our method achieved the top robustness among state-of-the-art methods on three long-term tracking datasets. These findings demonstrate that our method is effective for long-term object tracking and that its performance and speed are promising for use in practical applications of various technologies underlying object tracking.
Ontological aspects of the contradictions of artificial intelligence

This article is devoted to discussion of the philosophical aspects of the problems associated with the creation of a strong artificial intelligence. It is noted that even taking into account the fact that the study of the brain and consciousness are represented by separate areas, such as cognitive neuroscience and philosophy of consciousness, there are a number of problems of an ontological nature, which indicate that research in the field of creating systems with artificial intelligence, although actively developing, is rather related to its weak definition and does not provide prerequisites for confidence in the possibility of creating a strong artificial intelligence.
A Scale Invariant Measure of Flatness for Deep Network Minima

It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization. However, for deep networks with positively homogeneous activations, most measures of flatness are not invariant to rescaling of the network parameters. This means that the measure of flatness can be made as small or as large as possible through rescaling, rendering the quantitative measures meaningless. In this paper we show that for deep networks with positively homogenous activations, these rescalings constitute equivalence relations, and that these equivalence relations induce a quotient manifold structure in the parameter space. Using an appropriate Riemannian metric, we propose a Hessian-based measure for flatness that is invariant to rescaling and perform simulations to empirically verify our claim. Finally we perform experiments to verify that our flatness measure correlates with generalization by using minibatch stochastic gradient descent with different batch sizes to find deep network minima with different generalization properties.
Rethinking of Deep Models Parameters with Respect to Data Distribution

The performance of deep learning models are driven by various parameters but to tune all of them every time, for every given dataset, is a heuristic practice. In this paper, unlike the common practice of decaying the learning rate, we propose a step-wise training strategy where the learning rate and the batch size are tuned based on the dataset size. Here, the given dataset size is progressively increased during the training to boost the network performance without saturating the learning curve, which is seen after certain epochs. We conducted extensive experiments on multiple networks and datasets to validate the proposed training strategy. The experimental results proves our hypothesis that the learning rate, the batch size and the data size are interrelated and can improve the network accuracy if an optimal progressive step-wise training strategy is applied. The proposed strategy also reduces the overall training cost compared to the baseline approach.
Control of Single-Axis and Dual-Axis Solar Tracking System

This paper concerns with improving the overall output and stability of the solar system by Single-Axis Solar Tracking (SAST) and Dual-Axis Solar Tracking (DAST) by using MATLAB/SIMULINK package with the Arduino as the main control unit in the paper. This combination makes the system more powerful, helps in saving the panel and improve the output or productivity of solar power. The process of tracking makes the panel produce its maximum power all over the day because it makes the panel follow the sun where ever it goes. Voltage and power extracted from the panel will be greater than the stationary position generation depending on the type of tracking system. After finishing the work, it was discovered that: the dual-axis solar tracking system had improved the output of the stationary position by 3.3% and the single-axis solar tracking system had improved the output of the stationary position by 2.6%. The output percentage is low because we used an experimental cell which is not accurate, that is because our paper is not actually an improvement to the cell, but a system that controlling the cell. For the system, it proved its efficiency in producing more power than normal systems. And once using an accurate and capable cell, the output will be further improved, because using MATLAB, which made our system accurate in calculations, and able to analyze information in a better way.
Application: Names and the Mental Files Framework

Abstract This chapter continues the process of anthropocentric abstraction, here concentrating on proper names. Do AI systems use proper names? Using our example of ‘SmartCredit’, it highlights problems concerning how to treat the output of an AI system when some, but not all or most, of the information in its neural network fails to apply to the individual we interpret the output to be about. After giving reasons to think the standard Kripkean theory might not work well here, it suggests an alternative theory of communication about particular entities, the mental file framework, which is more apt for theorizing about AI systems. It then abstracts from the human-centric features of extant theories of mental files to consider how AI might use something like them to refer to particulars.
Theoretical Research and Framework Design of Spanish-Chinese Smart Translation System from Machine Learning Perspectives

Theoretical research and framework design of the Spanish-Chinese smart translation system from machine learning perspectives is done in this manuscript. Cyberspace has become the main virtual space of people's spiritual life. The main form of information in cyberspace contains pictures, and also image communication and image dissemination have become a new trend. To this, the machine learning perspective is applied for the construction of the smart model. A fuzzy information analysis model is established for the characteristic vocabulary translation, and combine related feature extraction and fuzzy information optimization control methods to realize online control of characteristic vocabulary translation. The machine learning algorithm is used to perform fuzzy control and convergence judgment on the online generation process of the featured vocabulary translation, and realize the optimization of the online generation process of featured vocabulary translation. The intelligent model is implemented through the analysis.
Research Tools, Survey Responses, and Interview Analysis from a Case Study of Onboarding Software Teams at Microsoft

This repository contains the supplementary material for our paper "A Case Study of Onboarding in Software Teams: Tasks and Strategies" at ICSE 2021. It contains interview guides, surveys, anonymized survey responses, and interview analysis and quotes. We have removed open-ended questions from survey responses to protect participants' privacy. All artifacts are publically available at https://doi.org/10.5281/zenodo.4455936.
On the Reconstruction of 3-Uniform Hypergraphs from Step-Two Degree Sequences

A nonnegative integer sequence is k-graphic if it is the degree sequence of a k-uniform simple hypergraph. The problem of deciding whether a given sequence \(\pi \) admits a 3-uniform simple hypergraph has recently been proved to be NP-complete, after long years of research. Thus, it is helpful to find which classes of instances are polynomially solvable in order to restrict the NP-hard core of the problem and design algorithms for real-life applications. Several necessary and few sufficient conditions for \(\pi \) to be k-graphic, with \(k\ge 3\), appear in the literature. Frosini et al. defined a polynomial time algorithm to reconstruct k-uniform hypergraphs having regular or almost regular degree sequences. Our study fits in this research line defining some conditions and a polynomial time algorithm to reconstruct 3-uniform hypergraphs having step-two degree sequences, i.e., \(\pi =(d,\dots ,d,d-2,\dots ,d-2)\). Our results are likely to be easily generalized to \(k \ge 4\) and to other families of similar degree sequences.
Breast Cancer Classification Based on Unsupervised Linear Transformation Along with Cos Similarity Machine Learning

In this research, we propose applying machine learning techniques on Breast Cancer classification problem that based on combination of unsupervised linear transformation along with Cos similarity machine learning. In unsupervised transformation we use principal components analysis (PCA). PCA used in analysing Breast cancer dataset to select effective features. In addition, we propose using classification model, Cos similarity to test accuracy of selected features. We prove the performance acceptability of the suggested PCA-Cos technique by conducting extensive practical experiments on binary-class Breast cancer dataset. The experimental results prove that PCA-Cos technique is promising method for manipulating features selection and cancer classification challenges and achieves the significant accuracy. Compared to previously suggested methods.
Exploring Visual-Audio Composition Alignment Network for Quality Fashion Retrieval in Video

Fashion retrieval in video suffers from the issues of imperfect visual representation and low quality of search results under the E-commercial circumstance. Previous works generally focus on searching the identical images from visual perspective only, but lack of leveraging multi-modal information for high quality commodities. As a cross-domain problem, instructional or exhibiting audio reveals rich semantic information to facilite the video-to-shop task. In this paper, we present a novel Visual-Audio Composition Alignment Network (VACANet) to deal with quality fashion retrieval in video. Firstly, we introduce the visual-audio composition module in VACANet aiming to distinguish attentive and residual entities by learning semantic embedding from both visual and audio streams. Secondly, a quality alignment training scheme is then designed by quality-aware triplet mining and domain alignment constraint for video-to-image adaptation. Finally, extensive experiments conducted on challenging video datasets demonstrate the scalable effectiveness of our model in alleviating quality fashion retrieval.
Co-Drive: Experiencing Social Virtual Travel on a Car Trip

The Co-Drive interactive experience at CHI2021 gives conference attendees the possibility to experience social virtual travelling by car either as the driver or the remote passenger. Through the dislocation of two prototypes in two different parts of the world, Co-Drive trips will be available in two different locations, one of which crowdsourced among prospective CHI attendees. After experiencing the Co-Drive trip, participants will be able to share their experience in a collective way through a virtual meeting held during the conference and subsequently in an individual way through a phone call or video/audio recording from their car to the main author.
WITHDRAWN: Information retrieval models in Neural Networks Framework: A survey

Information retrieval using neural networks both as shallow or deep neural networks is an emerging field. This paper presents a survey on neural network models used at various stages of information retrieval. Information Retrieval is taking a new dimension everyday with the abundant information available in World Wide Web and World Wide Web is acting as universal information repository of human knowledge. The paper focuses on text information retrieval and emphasizes the importance of context-based information retrieval.
Optimal human-machine collaboration for enhanced cost-sensitive biometric authentication

Despite growing interest in human-machine collaboration for enhanced decision-making, little work has been done on the optimal fusion of human and machine decisions for cost-sensitive biometric authentication. An elegant and robust protocol for achieving this objective is proposed. The merits of the protocol is illustrated by simulating a scenario where a workforce of human experts and a score-generating machine are available for the authentication of handwritten signatures on, for example, bank cheques. The authentication of each transaction is determined by its monetary value and the quality of the claimed author’s signature. A database with 765 signatures is considered, and an experiment that involves 24 human volunteers and two different machines is conducted. When a reasonable number of experts are kept in the loop, the average expected cost associated with the workforce-machine hybrid is invariably lower than that of the unaided workforce and that of the unaided machine.
Multistream Temporal Convolutional Network for Correct/Incorrect Patient Transfer Action Detection Using Body Sensor Network

The development of body sensor networks (BSNs) with rich multimodal signals has enabled highly accurate fine-grained action detection, which is the cornerstone of many humancomputer interaction applications. However, in the case of consecutive fine-grained actions, most existing wearable sensor-based detection methods are constrained by sliding windows because of their limited temporal receptive fields, and existing sequence-to-sequence detection methods cannot effectively leverage the potential of multimodal information of wearable sensors. Herein, to give multimodal signals full play in fine-grained action detection, we propose a novel temporal convolutional network by designing a channel attention-based multistream structure. We apply it to a promising application for correct and incorrect patient transfer nursing action detection. A dataset is collected from a BSN on a patient when nurses perform patient transfer. Extensive experiments on our dataset and public dataset (C-MHAD) demonstrate that the proposed method is superior to the state-of-the-art methods, because it can strengthen the utilization of prediction features from the more convincing modal stream at each time frame.
Application: Predication and Commitment

Abstract The previous chapters have given us ways of thinking about how an AI system might use names and predicates. But language use involves more than simply tokening expressions. It also involves predicating, or asserting, or judging: applying predicates to terms to make a claim. How can AI do that, even granting it can name things and express predicates? This chapter proposes an answer, by melding together two popular theories: the act-theory of propositional content, and teleosemantics. In a now familiar way, it abstracts from human-centric features of extant theories to show how we can understand AI predication.
Developing a Performance-based Tool for Arabic Text Steganography

Steganography is the practice of hiding a secret message within different cover mediums to preserve data secrecy. This paper focuses on developing a performance-based stego-object tool for transmitting secret Arabic texts over the Internet by considering three main performance parameters namely: capacity, security, and robustness. The proposed tool has three modules: the first one is for hiding a secret message within Arabic cover text using Kashida (extension character) and diacritics. The main objective of the second module is to compare and select the highest performance stego-object based on developed performance criteria. The third module is for extracting the secret message from the stego-object text. The criteria for selecting the best performance cover text is based on the input coefficients by the users according to their preferences. The proposed tool has a great contribution to the practical use of steganography for hiding secret messages based on performance measures, and it will open a new direction for future research.
Smart Glasses Using Arduino

The purpose of the smart glasses has always been the minimization of the distance between the physical and the digital world. The way human interaction with the mobile with help of wireless communication. Numerous ways and algorithms have been proposed and implemented to achieve the goal of “smart glasses”. This paper deals with design and implementation of smart glasses using Arduino / pro mini / micro / nano.The Hardware part consist of Arduino / pro mini / micro / nano, HC-05 (Bluetooth module) to wireless communicate with Smart phone, 0.96 oled display and the software part consist of the Arduino IDE.