Reviewerly/exHarmony huggingface dataset:
Research on Operation Index Management of Communication Network

With the application of a large number of new technology and equipment and the expansion of network scale, at present, the content and form of communication network operation index management have undergone tremendous changes. This paper mainly studies the current situation and related advanced technology of communication network operation index, introduces the management scope and content of communication network operation index in detail, and expounds the application and implementation process of communication network operation index management system.
On $$\alpha $$-points of q-analogs of the Fano plane

Abstract Arguably, the most important open problem in the theory of q -analogs of designs is the question regarding the existence of a q -analog D of the Fano plane. As of today, it remains undecided for every single prime power order q of the base field. A point P is called an $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -point of D if the derived design of D in P is a geometric spread. In 1996, Simon Thomas has shown that there always exists a non- $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -point. For the binary case $$q = 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , Olof Heden and Papa Sissokho have improved this result in 2016 by showing that the non- $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -points must form a blocking set with respect to the hyperplanes. In this article, we show that a hyperplane consisting only of $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -points implies the existence of a partition of the symplectic generalized quadrangle W ( q ) into spreads. As a consequence, the statement of Heden and Sissokho is generalized to all primes q and all even values of q .
More Efficient Shuffle Argument from Unique Factorization

Efficient shuffle arguments are essential in mixnet-based e-voting solutions. Terelius and Wikström (TW) proposed a 5-round shuffle argument based on unique factorization in polynomial rings. Their argument is available as the Verificatum software solution for real-world developers, and has been used in real-world elections. It is also the fastest non-patented shuffle argument. We will use the same basic idea as TW but significantly optimize their approach. We generalize the TW characterization of permutation matrices; this enables us to reduce the communication without adding too much to the computation. We make the TW shuffle argument computationally more efficient by using Groth's coefficient-product argument (JOC 2010). Additionally, we use batching techniques. The resulting shuffle argument is the fastest known \(\le \)5-message shuffle argument, and, depending on the implementation, can be faster than Groth's argument (the fastest 7-message shuffle argument).
Development of a Secured and Low-budget Biometric Electronic Voting Machine for Bangladesh

One of the fundamental goals of `Digital Bangladesh ' is ensuring people's democracy, human rights, transparency, and delivering government services through the maximum use of modern technologies. However, Bangladesh Election Commission is still striving to implement Electronic Voting Machines (EVM) in the parliamentary general election on an extended scale due to the lack of acceptance concerning EVM accuracy, easy accessibility, and security. This paper describes an implementation of a low-budget and securely accessible solution for the Electronic Voting Machine feasible for Bangladesh with the benefit of verifying the registered voters with a central database through a biometric identification process and maintaining the secrecy of the voters. The proposed system uses low profile microcontrollers (Arduino Uno), which reduces the device's cost to approximately half of the price of other available versions of EVM. The system can communicate with the database to store voter information and vote counts that make the system secure against any fraudulence during the election process and eliminate manual vote tallying problems.
Classification of Blockchain Implementation in Mobile Electronic Health Records and Internet of Medical Things Devices Research

Blockchain technology is disrupting the healthcare industry while the number of Internet of Medical Things (IomT) devices connected to the internet is increasing. Blockchain technology offers Electronic Health Records (EHR) and IoMT devices data security, integrity, auditability, and validity. This survey reviews the strengths and weaknesses of blockchain technology as they impact mobile EHRs and IoMT devices. Additionally, recent research on blockchain implementations in mobile EHRs and IoMT devices is reviewed and evaluated on how they meet the identified needs of EHRs and address the weaknesses of blockchains. Mobile health applications are highlighted if one was presented in the research.
P-Shake: Towards Secure Authentication and Communication between Mobile Devices

Ubiquitous mobile device applications exchange information through wireless channels (e.g., Bluetooth). However, the nature of the wireless channel raises multiple security communication problems (e.g., Man-in-the-Middle attack).In this paper, we propose P-Shake, a mechanism to achieve secure authentication and communication between mobile devices over insecure wireless channels, without any prior knowledge and synchronizing the internal clocks. In P-Shake, we first introduce the moving average filter and high-pass filter to mitigate the impacts of both tiny and relative movements of the mobile devices on the original acceleration data from accelerometers. Second, we employ Pearson correlation coefficient to authenticate the mobile devices by measuring the overall correlation of the acquired smoothed acceleration data. Third, we extract the common secrets between mobile devices to generate high-entropy session key. We make security analysis and extensive experiments to evaluate the security and performance of P-Shake. The results illustrate that P-Shake could achieve secure authentication and communication with higher feasibility than previous works.
Application of Information Security in Image Encryption Processing Based on Big Data

Abstract With the widespread application of Internet technology, digital image encryption needs to be completed. But image is different from text, it has many inherent characteristics, such as large amount of image data, high redundancy, strong correlation between adjacent pixels. Therefore, the traditional symmetric encryption algorithm data encryption standard and advanced data addition standard will no longer be suitable for image encryption. Based on the concept of information security and image encryption, a new image processing method is proposed in this paper, which is based on the logistic tent sine composite algorithm model. The compression ratio is 75%. When the wrong key is input, the PSNR of the algorithm reconstruction result is only about 5dB, and the reconstruction error error is only 1, so the encryption effect of the model is better.
Crime Forecasting Using Time Series Analysis

With the advent of computers and the rapidly increasing speed of technological advancements, data is now being collected at breakneck speeds. Accelerating increase in the amount and speed at which data is collected has been matched with advancements in data storage and data analysis technology. Crime data has been steadily collected for many decades now and can be used to analyze and predict novel and interesting patterns as they emerge. With this new technology, we can forecast crimes and crime rates to help law enforcements authorities. This paper uses predictive data analysis and forecasting to predict future crime rates.
A conceptual framework for adaptive security

This paper discusses the current needs for adaptive security. It then proposes a conceptual framework that can be applied by security management to establish feedback loops that can be used to better inform security decisions. We discuss some approached that we believe will yield results for security. Furthermore, we briefly discuss a case study and some initial results that this approach provided for security in this organization. Finally, there is a small discussion regarding limitations and possibilities of the framework.
Power System State Estimation Based on PLS-ELM

Abstract In order to effectively improve the computational accuracy and robustness of state estimation, a power system state estimation method based on partial least square (PLS) and extreme learning machine (ELM) is proposed in this paper, which combines artificial intelligence technology with grid data. In order to solve the problem of strong correlation between measurements, PLS is used to extract important information and select variables for each measurement, and the optimal variables are input into the ELM model, thus the PLS-ELM model of state quantities is established. Finally, this method is compared with other methods. Experimental results show that the proposed method reduces the complexity of the model, and has high estimation accuracy and strong robustness.
Speech Database in Sylheti and Speech Recognition using Convolutional Neural Network

Speech dataset is the primary and core element for a speech/speaker recognition system specific to a language. Sylheti, a language of Indo-Aryan family, is a member of under-resourced language group which is spoken in Sylhet district of Bangladesh and Barak valley part of Assam, India. It does not have any electronic and web data resources. So, there is a requirement to design a speech database in Sylheti. As the speech database construction is an ongoing process, the preliminary task of developing a small vocabulary new speech database in Sylheti is presented in this paper. Thereafter, implementation of two Automatic Speech Recognition systems for the Sylheti language by using convolutional neural network are reported here.
Model-based Clustering: Basic Ideas

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Chapter 3. Why does D-linking reduce the need for inversion in Spanish wh-questions?

Spanish wh-questions without inversion are much more acceptable when the wh-phrase is D-linked. Standard semantic and syntactic analyses of D-linking, developed for the more well-known case of D-linking in weak islands, make incorrect predictions regarding the Spanish case, while analyses based on working memory fare better. In these analyses, the effect obtains because at the time when the gap is posited, the processor is able to retrieve a D-linked filler more easily, and this results in increased acceptability. This type of analysis makes correct predictions about D-linking in Spanish wh-questions, and the Spanish facts provide new evidence that such an approach to D-linking based on working memory is correct. The analysis adopted leaves open the question of the proper analysis of inversion itself. Even if D-linking results from properties of working memory, the inversion phenomenon could still be the result of a grammatical constraint.
Real-time face view correction for front-facing cameras

Abstract Face views are particularly important in person-to-person communication. Differenes between the camera location and the face orientation can result in undesirable facial appearances of the participants during video conferencing. This phenomenon is particularly noticeable when using devices where the front-facing camera is placed in unconventional locations such as below the display or within the keyboard. In this paper, we take a video stream from a single RGB camera as input, and generate a video stream that emulates the view from a virtual camera at a designated location. The most challenging issue in this problem is that the corrected view often needs out-of-plane head rotations. To address this challenge, we reconstruct the 3D face shape and re-render it into synthesized frames according to the virtual camera location. To output the corrected video stream with natural appearance in real time, we propose several novel techniques including accurate eyebrow reconstruction, high-quality blending between the corrected face image and background, and template-based 3D reconstruction of glasses. Our system works well for different lighting conditions and skin tones, and can handle users wearing glasses. Extensive experiments and user studies demonstrate that our method provides high-quality results.
CLENet: A Context and Location Enhanced Transformer Network for Target-Oriented Sentiment Classification

Target-oriented sentiment classification seeks to predict the sentiment polarity of a target in a given text. Previous approaches mainly focus on LSTM-attention structure, but practice shows that LSTM and attention mechanism can not correctly identify and classify partial negation relations and multi-target scenarios on limited datasets. This paper proposes a context and location enhanced transformer network that improves both the modeling of negation relations and its classification accuracy on multi-target scenarios. Experimental results on SemEval 2014 and Twitter datasets confirm the effectiveness of our model.
Rule Extraction from Neural Networks and Other Classifiers Applied to XSS Detection

Explainable artificial intelligence (XAI) is concerned with creating artificial intelligence that is intelligible and interpretable by humans. Many AI techniques build classifiers, some of which result in intelligible models, some of which don’t. Rule extraction from classifiers treated as black boxes is an important topic in XAI, that aims to find rule sets that describe classifiers and that are understandable to humans. Neural networks provide one type of classifier where it is difficult to explain why the inputs map to the decision; support vector machines provide a second example of this kind. A third type of classifier, k-nearest neighbour (k-NN), gives more interpretable classifiers, but suffers from performance problems as the model is little more than a representation of the training data. This work investigates a technique to extract rules from classifiers where the underlying problem’s feature space is Boolean, without looking at the inner structure of the classifier. For such a classifier with a small feature space, a Boolean function describing it can be directly calculated, whilst for a classifier with a larger feature space, a sampling method is investigated to produce rule-based approximations to the behaviour of the underlying classifier, with varying granularity, leading to XAI. The behaviour of the technique with neural network, support vector machine, and k-NN classifiers is experimentally assessed on a dataset of cross-site scripting (XSS) attacks, and proves to give very high accuracy and precision, often comparable to the classifier being approximated.
Research on Cognitive Effects of Internet Advertisements Based on Eye Tracking

Thanks to eye tracking systems, movement data of eyes can be obtained when audiences gaze at advertisements. Eye tracking emerges as an effective means to obtain cognitive effects. Advanced techniques generally require customized equipment and systems. Meanwhile, acquisition of experimental data costs too much. It greatly limits the application scope of eye tracking systems. In this paper, our work consists of two parts. For the first part, we study the cognitive effect of ads through tracking the eyes of participants taking part in our experiments. In the other part, due to the limitation of the eye tracking, we propose a new eye tracking system based on edge computing to extend the application range. The experiment results show that products’ involvement and sexual appeal have an impact on the cognitive effect. Furthermore, by taking advantage of the new eye tracking system, we believe it can make psychological research easier.
First-Order Geometric Multilevel Optimization for Discrete Tomography

Discrete tomography (DT) naturally leads to a hierarchy of models of varying discretization levels. We employ multilevel optimization (MLO) to take advantage of this hierarchy: while working at the fine level we compute the search direction based on a coarse model. Importing concepts from information geometry to the n-orthotope, we propose a smoothing operator that only uses first-order information and incorporates constraints smoothly. We show that the proposed algorithm is well suited to the ill-posed reconstruction problem in DT, compare it to a recent MLO method that nonsmoothly incorporates box constraints and demonstrate its efficiency on several large-scale examples.
Vehicle Classification from Profile Measures

This paper proposes two novel convolutional neural networks for 3D object classification, tailored to process point clouds that are composed of planar slices (profiles). In particular, the application that we are targeting is the classification of vehicles by scanning them along planes perpendicular to the driving direction, within the context of Electronic Toll Collection. Depending on sensors configurations, the distance between slices can be measured or not, thus resulting in two types of point clouds, namely metric and non-metric. In the latter case, two coordinates are indeed metric but the third one is merely a temporal index. Our networks, named SliceNets, extract metric information from the spatial coordinates and neighborhood information from the third one (either metric or temporal), thus being able to handle both types of point clouds. Experiments on two datasets collected in the field show the effectiveness of our networks in comparison with state-of-the-art ones.
WITHDRAWN: Artificial intelligence email automater and insights generator of credit card customers based on unsupervised learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our-business/policies/article-withdrawal ). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
The impact of probability on archaeological interpretation

Interpretation of the archaeological record is determined by the occurrence and distribution of finds and their (relative and absolute) distribution over space. Comparison and categorization of finds strongly depends on their characteristics and the significance assigned to them. By default this is a very erroneous process leading to debate about individual finds, their significance and of course their interpretation. In this paper we will discuss the (objective) impact of probability on interpretation of data by measuring and comparing the impact of probability on metrics such as centrality.
Implementation of the model of remote professional training of police officers by the University of the Ministry of Internal Affairs of Russia

Admission of an employee of the internal affairs bodies to the independent performance of official duties in the position he holds in the territorial body of the Ministry of Internal Affairs of Russia is possible only after passing professional training. In the past the interior Ministry and its territorial bodies of internal affairs reform has been reduced a significant amount of vocational training centres of territorial bodies of the MIA of Russia in the constituent entities of the Russian Federation with the laying on of responsibilities implementation of programmes of professional training of police officers at the universities of the interior Ministry. The pandemic restrictions imposed on the territory of the Russian Federation have translated this training into a remote format. This article discusses various aspects of professional training of police officers, implemented by educational organizations of higher education of the Ministry of Internal Affairs of Russia using the model of distance learning activities.
Can Reinforcement Learning Lead to Healthy Life?: Simulation Study Based on User Activity Logs

The importance of developing an application based on intervention technology that leads to a healthier life is widely recognized. A challenging part of realizing the application is the need for planning, i.e., considering a user's health goal (e.g., sleep at 10:00 p.m. to get enough sleep), providing intervention at the appropriate timing to help the user achieve the goal. The reinforcement learning (RL) approach is well suited to this type of problem since it is a methodology for planning; RL finds the optimal strategy as that which maximizes future expected profit. The purpose of this study is to clarify the effects of intervention based on RL to support healthy daily life. Therefore, we (i) collect real daily activity data from participants, (ii) generate a user model that imitates the user's response to system interventions, (iii) examine valuable goals and design them as rewards in RL and (iv) obtain optimal intervention strategies by RL via simulations given a user model and goals. We evaluate a generated user model and verify by simulations whether our method could successfully achieve the goal. In addition, we analyze the cases that demonstrated higher probability of achieving the goal and report the features.
Improved Multi-label Medical Text Classification Using Features Cooperation

Medical text categorization is a valuable area of text classification due to the massive growth in the amount of medical data, most of which is unstructured. Reading and understanding the information contained in millions of medical documents is a time-consuming process. Automatic text classification aims to automatically classify text documents into one or more predefined categories according to several criteria such as the type of output (multi-label or mono label). Feature extraction task plays an important role in text classification. Extracting informative features highly increases the performance of the classification models and reduces the computational complexity. Traditional feature extraction methods are based on handcrafted features which mainly depend on prior knowledge. The use of these features may involve an insignificant representation. Doc2vec is a way to generate a vector of informative and essential features that are specific to a document. In this paper, the impact of combining handcrafted and doc2vec features in the multi-label document classification scenario is analyzed by proposing a system named MUL-MEDTEC. The one-versus-all classification strategy based on logistic regression is adopted in this study to predict for each medical text it to one or several labels. Experimental results based on Ohsumed medical dataset are very encouraging with based classification accuracy equal to 0.92 as global precision.