Reviewerly/exHarmony huggingface dataset:
ML-SPD: Machine Learning based Sentiment Polarity Detection

Internet revolution creates very important trends in people's life like news-portals, online-education, home-offices, online shopping, social media, etc. Without any controversy, social media is one of the most important outcomes of the Web. Today, social media is more than a communication channel where people have the opportunity to express their feelings, write their comments on microblogging sites, discussion groups, review sites, etc. These common habits have resulted in two important consequences: 1) Accumulation of very huge data on online platforms, 2) The requirement of automatic systems to classify these accumulated big data by subjective and sentimentally. In many cases, Sentiment Polarity Detection (SPD) in text may be an urgent requirement, rather than identifying the subject of the text. For instance, positively or negatively labeled product reviews may give sufficient summary information to readers about the review. In this study, to solve SPD problem we explore different text representation models in conjunction with state-of-the-art traditional Machine Learning techniques: Support Vector Machines (SVM), Neural Networks (NN), Nave Bayes (NB), and combination of NB and SVM classifier (NBSVM). We perform experiments on three publicly available benchmark movie review datasets in different languages: CornellPD in English, HUMIR in Turkish and SerbSPD-2C in Serbian. Experimental results confirm that the presented techniques achieve improvements over the previously published techniques applied to movie reviews datasets in Turkish and English. Developed software package “ML-SPD” is made publicly available to the research community so it can serve as a good baseline for future research.
The future of radio

Audio streaming services are used daily by millions worldwide, enabling on-demand listening and the discovery of songs, artists and podcasts that closely align with the listener's preferences. Meanwhile, traditional terrestrial radio persists as another ubiquitous and still viable mode of accessing more pre-programmed music and news content, including traffic reports and weather information. While both media services offer listeners a distinct set of value propositions, efforts to combine the 'best of both worlds' have been few and far between. Towards this objective, we describe our preliminary efforts to understand audio media consumers' music streaming and traditional radio listening habits and preferences as part of a project aimed at creating an integrated experience for individual listeners and their close networks of family and friends. Through rapid prototyping, and the speed dating method, we explore the design implications for creating and validating radio-like experiences that are at once personal, customizable and shareable.
Evolutionary Extreme Learning Machine Weighted Fuzzy-Rough Nearest-Neighbour Classification

Due to the mechanism of the instance-based classification, the feature significance plays an important role in the nearest-neighbour classification tasks. The existence of the irrelevant features would degrade the performance of these algorithms by mischoosing the nearest neighbours. However, these irrelevant features are normally inevitable in the real applications. In this paper, the evolutionary extreme learning machine (E-ELM) algorithm is employed to distinguish the feature significance for the fuzzy-rough nearest-neighbour (FRNN) method. This hybrid learning approach, entitled evolutionary extreme learning machine weighted fuzzy-rough nearest-neighbour algorithm, extracts the feature significance by integrating the parameters from the parallel input node to the output node in E-ELM. Such feature significance is transformed to implement a weighted FRNN method to perform the classification tasks. Systematic experimental results, for both dimensionality reduction and classification problems, demonstrate that the proposed approach generally outperform many state-of-the-art machine learning techniques.
File Forgery Detection Using a Weighted Rule-Based System

The society is becoming increasingly dependent on digital data sources. However, our trust on the sources and its contents is only ensured if we can also rely on robust methods that prevent fraudulent forgery. As digital forensic experts are continually dealing with the detection of forged data, new fraudulent approaches are emerging, making it difficult to use automated systems. This security breach is also a good challenge that motivates researchers to explore computational solutions to efficiently address the problem. This paper describes a weighted rule-based system for file forgery detection. The system was developed and validated in the several tasks of ImageCLEFsecurity 2019 track challenge, where promising results were obtained.
Banknote Infrared Characteristics Discrimination Based on PCA and LDA

Infrared characteristic identification on banknote is an important research topic in the banknote discrimination field. In this paper we combined two successful approaches called Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to study this, we are able to improve the precision of infrared characteristic identification. Firstly, using the PCA reduces the data dimensionality of infrared banknote images. Then the LDA which has the characteristics of making the largest distinct distance between classes and the smallest cluster distance within one class, separates infrared images of the different version real banknotes as far as possible and clusters infrared images of the one version real banknotes as near as possible with training. Finally, the banknote discrimination is implemented by calculating the distances of features. The experimental results illustrate that the proposed algorithm obtains high recognition rate and meets the real-time requirement of the banknote discrimination system.
Investigation of effect of different virtual reality environments on saccadic eye movement: Construct validity testing in healthy older adults

This paper describes two experiments designed to (1) ascertain whether the way in which architectural plans are displayed on a computer screen influences the quality of their correction by humans, and (2) identify the visual exploration strategies adopted in this type of task. Results of the first “spot the difference” experiment showed that superimposing the plans yielded better error correction performances than displaying them side by side. Furthermore, a sequential display mode, where the second plan only gradually appeared on the screen, improved error search effectiveness. In the second experiment, eye movement recordings revealed that superimposition increased plan comparison efficiency by making it easier to establish coreference between the two sources of information. The improvement in effectiveness in the sequential condition was shown to be linked to the attentional guidance afforded by this display mode, which helped users to make a more thorough exploration of the plans.
Joint Generic Learning and Multi-source Domain Adaptation on Unsupervised Face Recognition

For most machine learning algorithms, the performance of model trained on the source domain degenerates obviously when it is applied to the target domain because the distributions of both domains are different. Besides, in many cases, there is a lack of adequate supervised target domain training samples. In order to solve the mismatch between the domains and the lack of training samples, this paper proposes jointing domain adaptation and generic learning on solving unsupervised face recognition. Firstly, samples are selected randomly from multiple source and target domains which do not contain interest subjects and construct multiple sub-datasets. Secondly, learning a common subspace for each sub-dataset. In the common subspace, source and target domains can mutual interlace and their structures are well preserved, and we can get more discrimination information from multiple feature subspaces. Finally, the recognition is obtained by using combine strategies. The experimental results show that the recognition performance of the framework is better than that of the competitive ones.
Specification and Construction of the Perceptron Multicapa Model

The objective of this chapter is to analyze the problem surrounding the task of prognosis with neural networks and the factors that affect the construction of the model, and that often lead to inconsistent results, emphasizing the problems of selecting the training algorithm, the number of neurons in the hidden layer, and input variables. The methodology is to analyze the forecast of time series, due to the growing need for tools that facilitate decision-making, especially in series that, given their characteristics of noise and variability, infer nonlinear dynamics. Neural networks have emerged as an attractive approach to the representation of such behaviors due to their adaptability, generalization, and learning capabilities. Practical evidence shows that the Delta Delta and RProp training methods exhibit different behaviors than expected.
Integrity Measurement Method for Key Business Code Segments in Power Dispatching Cloud Control System

In order to ensure the safe and healthy operation of the power dispatching cloud control system, an integrity measurement method for key business code segments in the power dispatching cloud control system is proposed to address the security of the key services in the cloud environment. The method can verify the integrity of the code segment of the process running in the virtual machines at the VMM layer in an agentless manner without the need to ensure that all the code segments of the process are loaded into memory. This proposed method can provide better security for the key business of power dispatching control system to ensure stability in the business program with no interruption and malicious tampering. The experiments show that the proposed method has better applicability and flexibility compared with other methods of the same type.
Non-pre-trained Mine Pedestrian Detection Based on Automatic Generation of Anchor Box

Mine pedestrian detection is an important part of computer vision and one of the key technologies of unmanned locomotive. In order to improve the structural adaptability of pedestrian detection network, reduce the workload of pre-training and reduce the risk of “negative migration” brought by migration learning, a non-pre-training underground pedestrian detection network based on anchor box is proposed. Firstly, the network model of mine pedestrian detection is introduced, including non-pretrained backbone network and branch structure detection network. Among them, the non-pre-trained backbone network mainly adds BatchNorm operation to make the gradient more stable and smooth. Anchor location prediction branch and anchor shape prediction branch in the detection network work together to improve the regression accuracy of anchor box. Secondly, the loss function of network training is described and the training parameters are adjusted by weighted loss. Finally, the experimental results based on the video of Taoyuan and Xinji mine in Anhui province are given. The experimental data show that the proposed algorithm can still maintain 96.3% AP at a real-time processing rate of 24 FPS. Compared with the RefineDet512, the AP increases by 2.4%.
Robustness of Imperfect Initialization in Quantum Parameter Estimation

One of the most concerned problems in quantum metrology is exploiting quantum resources to increase the precision of quantum parameter estimation. One natural and standard way of improving the precision of estimation is letting the initial probe state being optimal. However, the optimal initial probe state on the one hand may be hard to identify, and on the other hand one cannot prepare the optimal initial state perfectly in practice. In this paper, we focus on the influence of the imperfect preparation of the optimal initial probe state to the precision limit of quantum parameter estimation. The analytical results are valid for unitary evolutions with arbitrary dimension.
Application of C # based Host Computer in Path Tracking of Unmanned Clean Vehicle

This paper presents a more convenient and accurate method to verify the reliability of the path tracking control system of the unmanned cleaning vehicle. With reference to back stepping algorithm, the path curve is drawn and sampled as feature points, which is then transformed into motion parameters to control the motion of the cleaning car. According to the drawing path, the speed and angular velocity are calculated, and compared with the actual parameters and motion trajectory, the error of the relevant parameters is obtained, and then the accuracy of the path tracking control of the unmanned cleaning vehicle is verified.
The Evaluation of RFF Authentication for Edge Computing

The radio frequency fingerprint (RFF) generated by the wireless terminal devices can can be used to identify authentication of wireless devices, which is an asymmetric authentication method and is specilly suitable the edge computing system that can provide the computing and energy power to perform the authentication cost. One of the key issues for the RFF authentication is its performance evaluation. In this paper, by taking advantages of the category separability measure method, a new method to evaluate the performance of RFF is proposed. The feasibility of the method is verified by experimental simulation.
Time Sequence based AES S-box Implementation Cryptoanalysis using Deep Learning Approaches

So far profiling attack on block ciphers is the most effective side channel analysis method. Conventional profiling attack approaches are always dependent on the evaluation models coordinating with target implementation devices. In this paper, we concentrate on exploring the routine of analysis the profile of generalized instruction execution on generalized computing platforms. We analyse and compare the basic routine of profiling attack using template attack, machine learning based attack and deep learning approaches, and take full advantage of deep learning approaches to make a time sequential AES S-box implementation cryptoanalysis. We use an accurate register operation timer to record the byte substitution of a multiple rounds, and use the deep neural network to recover the target key. According to our experiments, our approaches are more reliable, generalized, and can achieve at least the same performance as the conventional attack routines.
The light

This work explores the potential of a set comprised of wearable sensors, a performative lighting installation, and a public museum space, to inspire performative and collaborative social behavior among members of the public. Our installation, The Light, was first exhibited as part of the Late at Tate Britain event in 2019. In this paper we discuss the concept and technological implementation behind the work, and present an initial qualitative study of observations made of the people who interacted with it. The study provides a subjective evaluation based on people's facial expressions and body language as they improvise and coordinate their movements with one another and with the installation.
Poster: Learn to Love My Grandchild Design-with-the-User

This paper is part of a PhD study. Parents and caregivers are the first teachers of young children; and therefore, play a crucial role in transforming education in the early years. The majority of South African parents are living in poverty, and because of their circumstances, they struggle to build relationships with their children. How to offer support programmes to parents living in poverty, at scale and in an affordable way, remains elusive. ‘Design-with-the user’ approach suggests building and developing programmes with the user throughout the project cycle. To answer the research question of who is the receiver of the message, I zoom into the life of one of the case study participants and specifically, what is the story encompassing her characteristics, needs and challenges. The results show that m-learning programmes designed and implemented in other countries cannot just be transferred into a new local context without adaptation and considering designing with the user.
AHINE: Adaptive Heterogeneous Information Network Embedding

Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensional vectors such that the graph structural information and properties are maximumly preserved. Many prior works focused on embeddings for networks with the same type of edges or vertices, while some works tried to generate embeddings for heterogeneous network using mechanisms like specially designed meta paths. In this paper, we propose novel Adaptive Heterogeneous Information Network Embedding (AHINE), to compute distributed representations for elements in heterogeneous networks. Specially, AHINE uses an adaptive deep model to learn network embeddings that maximizes the likelihood of preserving the relation chains not only between adjacent nodes but also between non-adjacent nodes. We apply our embeddings to a large network of points of interest (POIs) and achieve superior accuracy on some prediction problems on a ride-hailing platform. In addition, we show that AHINE outperforms state-of-the-art methods on a set of learning tasks on public datasets, including node labelling and similarity ranking in bibliographic networks.
Digital Optical Neural Networks for Large-Scale Machine Learning

We propose a digital incoherent optical neural network architecture using the passive data routing and copying capabilities of optics for artificial neural network acceleration. We demonstrate a proof-of-concept experiment and analyze optimal use cases.
Stochastic Differential Equations in Machine Learning

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Web archiving: Policy and practice

Author(s): Christensen, Marlayna K.; Maches, Tori | Abstract: The UC San Diego Library has been collecting and providing access to archived web content since 2007. Initial collections were created on an ad hoc basis, with no highlevel plan to identify websites and content of interest, and there was little documentation of how early collection decisions were made. As time passed, the library’s web archiving efforts increased in scale, and outgrew this informal approach. Efforts were made to standardise web archiving processes and policies via collection request forms and standardised metadata, eventually culminating in the creation of a web archive collection development policy, and collection and quality control workflows and tracking. This article outlines the process of creating these tools, including establishing institutional needs and concerns, evaluating the wider landscape of web archiving policies and norms, and considering sustainable use of available resources. The article also discusses future areas of work to ensure that web content of research and historical interest is captured in full, preserved responsibly, and made accessible even when the original websites have changed or disappeared.
Psychoanalytical Approach to Transnational Money Laundering Utilizing Japanese Mobile Online Games with Gacha System: A Forecasting Study

The problems to face money laundering acts are the methods which the criminals are using that keeps on changing and improving along with the development of modern technology. While law enforcement mostly trying to prevent it by tampering by the technology used, there is a blind spot where there exists the reason of why and how the crime was possible in the first place. Some even neglected and underestimated by the authorities because of how the society views it in general, this is the recklessness the criminals can exploit. While money laundering utilizing Online Games, moreover with a Gacha system may sounds as bizarre as it is, it can be done and this article is intended to explain the what, why, who, and how of it. Using the Lacanian Psychoanalysis as a problem dissecting tool, the key here is to explain what makes the condition and environment where such crime is possible, involving the Otaku Subculture part of society in Japan and Indonesia, the companies behind it, and the Mobile Online Games itself. This article is forecasting the problems that will emerges concerning the crime of money laundering and its methods.
Restructured Cloning Vulnerability Detection Based on Function Semantic Reserving and Reiteration Screening

Although code cloning may speed up the process of software development, it could be detrimental to the software security as undiscovered vulnerabilities can be easily propagated through code clones. Even worse, since developers tend not to simply clone the original code fragments, but also add variable and debug statements, detecting propagated vulnerable code clone is challenging. A few approaches have been proposed to detect such vulnerability- named as restructured cloning vulnerability; However, they usually cannot effectively obtain the vulnerability context and related semantic information. To address this limitation, we propose in this paper a novel approach, called RCVD++, for detecting restructured cloning vulnerabilities, which introduces a new feature extraction for vulnerable code based on program slicing and optimizes the code abstraction and detection granularity. Our approach further features reiteration screening to compensate for the lack of retroactive detection of fingerprint matching. Compared with our previous work RCVD, RCVD++ innovatively utilizes two granularities including line and function, allowing additional detection for exact and renamed clones. Besides, it retains more semantics by identifying library functions and reduces the false positives by screening the detection results. The experimental results on three different datasets indicate that RCVD++ performs better than other detection tools for restructured cloning vulnerability detection.
Space-efficient algorithms for computing minimal/shortest unique substrings

Given a string T of length n, a substring u=T[i..j] of T is called a shortest unique substring (SUS) for an interval [s,t] if (a) u occurs exactly once in T, (b) u contains the interval [s,t] (i.e. i≤s≤t≤j), and (c) every substring v of T with |v|<|u| containing [s,t] occurs at least twice in T. Given a query interval [s,t]⊂[1,n], the interval SUS problem is to output all the SUSs for the interval [s,t]. In this article, we propose a 4n+o(n) bits data structure answering an interval SUS query in output-sensitive O(occ) time, where occ is the number of returned SUSs. Additionally, we focus on the point SUS problem, which is the interval SUS problem for s=t. Here, we propose a ⌈(log2⁡3+1)n⌉+o(n) bits data structure answering a point SUS query in the same output-sensitive time. We also propose space-efficient algorithms for computing the minimal unique substrings of T.