Reviewerly/exHarmony huggingface dataset:
Blind Detection of BCH Length from Noisy Received Vectors

In this paper, the goal is the length detection of BCH codes from noisy vectors received through a Binary Symmetric Channel (BSC) with cross over probability τ. The proposed method seeks to recover the length of code based on linear properties of cyclic codes. The received bitstream is divided into different likely lengths, and the algorithm puts the divided vector and its shifts in distinct rows of a matrix A. The rank of Λ is calculated, and detector makes a decision based on the rank value, while the rank of the matrix which contains codewords cannot exceed the length of message k /. Therefore, minimum rank is chosen as a sign of linear properties and codewords space. The operation is practical because of the low complexity of calculating the rank, and it can be done over GF(2) and modulo 2 addition.
State-Space Randles Cell Model for Instrument Calibration

It is desirable to calibrate electrochemical impedance spectroscopy (EIS) instrumentation using a Randles circuit. This presents a challenge as realistic loads, simulated by this circuit, contain theoretical components (Warburg elements) that are difficult to model. This paper proposes a state-space solution to this problem that facilitates digital realizations of high accuracy Randles circuit approximations for the purposes of verifying and calibrating instrumentation. Using Valsa, Dvořák, and Friedl's network approximation of a Warburg element, a collection of state-space relations describing the impedance of a Randles circuit are derived.
Learning hidden Markov models from aggregate observations

In this paper, we propose an algorithm for estimating the parameters of a time-homogeneous hidden Markov model (HMM) from aggregate observations. This problem arises when only the population level counts of the number of individuals at each time step are available, and one seeks to learn the individual HMM from these observations. Our algorithm is built upon the classical expectation–maximization algorithm and the recently proposed aggregate inference algorithm (Sinkhorn belief propagation). We present the parameter learning algorithm for two different settings of HMMs: one with discrete observations and one with continuous observations, and the algorithm exhibits convergence guarantees in both cases. Moreover, our learning framework naturally reduces to the standard Baum–Welch learning algorithm for HMMs when the population size is 1. The efficacy of our algorithm is demonstrated through several numerical experiments.
From Practice to Theory: The “Bright Illumination” Attack on Quantum Key Distribution Systems

The “Bright Illumination” attack [Lydersen et al., Nat. Photon. 4, 686–689 (2010)] is a practical attack, fully implementable against quantum key distribution systems. In contrast to almost all developments in quantum information processing (for example, Shor’s factorization algorithm, quantum teleportation, Bennett-Brassard (BB84) quantum key distribution, the “Photon-Number Splitting” attack, and many other examples), for which theory has been proposed decades before a proper implementation, the “Bright Illumination” attack preceded any sign or hint of a theoretical prediction. Here we explain how the “Reversed-Space” methodology of attacks, complementary to the notion of “quantum side-channel attacks” (which is analogous to a similar term in “classical”—namely, non-quantum—computer security), has missed the opportunity of predicting the “Bright Illumination” attack.
An Analytic Propositional Proof System on Graphs

In this paper we present a proof system that operates on graphs instead of formulas. Starting from the well-known relationship between formulas and cographs, we drop the cograph-conditions and look at arbitrary undirected) graphs. This means that we lose the tree structure of the formulas corresponding to the cographs, and we can no longer use standard proof theoretical methods that depend on that tree structure. In order to overcome this difficulty, we use a modular decomposition of graphs and some techniques from deep inference where inference rules do not rely on the main connective of a formula. For our proof system we show the admissibility of cut and a generalisation of the splitting property. Finally, we show that our system is a conservative extension of multiplicative linear logic with mix, and we argue that our graphs form a notion of generalised connective.
Real-world Applications of Mobile Learning Tools in Engineering: Prospects, Hindrances and Accessibility in Conjunction with Scholastic Views

This desk-based study uses a qualitative approach to explore the range of affordances that mobile technology-enhanced learning can avail to engineering education. It took documented outcomes and experience of mobile users who participated in engineering experiments. The secondary data are from eleven examined articles that are used in text data analysis. This study produces a scholar's view through empirical studies of real-world uses of mobile technologies in implementing learning. This study highlights the features and issues associated with mobile devices used in investigated studies. The summary of the study will provide insight for a prospective integration of elements of mobile technology in this area of engineering learning. It will be useful to learners and instructors for aligning choices of instruments suitable for supplementing `real' laboratories. The array of descriptions in this study ingresses and anticipates the relevance of this study to be helpful to anyone who intends to advise a mobile application pathway for learning, especially for engineering program.
S3GRN: Structural Similar Stepwise Generative Recognizable Network for Human Action Recognition With Limited Training Data

Human action recognition is a hot topic and it has been applied to various fields. Deep learning is one of the techniques in human action recognition which has achieved good results. However, the task is still challenging due to the less collected samples. In order to address this challenge and improve the recognition accuracy, the stepwise generative recognizable network is proposed based on the generative adversarial network, which can be used to expand limited training samples and then recognize. Firstly, the stepwise generative recognizable network is designed to combine the function of images generation and recognition for human action. Secondly, the structural similar constraint is introduced to stepwise generative recognizable network, called structural similar stepwise generative recognizable network, which can compare the similarity of generated images with real data to improve quality and diversity of generated images. Finally, the performance of proposed networks is verified by common databases and the self-build database which is collected in daily life. We achieved 97.14%, 94.88% and 99.69% recognition accuracy on MNIST, Weizmann and self-build dataset, respectively. The experimental results show that the combination of generation and recognition can improve the recognition accuracy without abundant training data, and the structural similar constraint not only can improve the quality and diversity of generated images but also perform better in convergence. The structural similar stepwise generative recognizable network reduces the workload of manual collection and solves the problem of lower recognition accuracy for limited training samples, which achieves the characteristics of natural expanded samples.
Design of Dynamic Cooperative Robot System for Financial Bill Recognition

This paper synthesizes the findings of application carried out in recent years and looks at the influence of automatic robots on financial scenario. Robot autonomous sorting can greatly improve the efficiency of perform tasks that require manual labor and free financial staffs from simple and single repetition work. It is estimated that robots are 15 times more efficient than human and the comprehensive cost is only 8% based on three-year workload. Starts from this background, a systematic description was carried out to analyze the use of dynamic cooperative robots in financial bill recognition and classification, while the opportunities, potential benefits and challenges to implementing dynamic cooperative robots in financial work was also discussed. The dynamic cooperative robot is consisted of mechanical arm, bill recognition system, cloud processing server and support multiple areas financial work. This work is the first to identify and discuss how financial work processed with all automatic systems and serves as a catalyst to spur future research in financial bill processing area.
The information-seeking behavior of aquatic science librarians in response to a query

When searching for information in response to a query, their own library’s online public access catalog (OPAC) and databases remain the most preferred resource for most aquatic science librarians. This preference does not vary regardless of age, gender, educational degree, position or designation, country classification, or type of library. While the Internet acts as a supplement to the library collection, whenever the requested information is not available at the library, librarians prefer Googling instead of looking at other sources. Other than aquatic science-specific open access repositories and databases, some of the librarians have identified Google Scholar, DOAJ, ResearchGate, and Academia, among many others as valuable resources that help them fulfill their clientele’s query.
Data as memory: Contemporary memory collection practices in extended interiors

This essay puts interiority to work as it examines the transformation from analogue memory collection housed in physical artifacts to the contemporary practices of digital memory collection where memory is packaged and preserved as data. Smartphones in particular offer a different mode of connectivity to extended interior spaces as technology re-defines the collection memory and the ways in which we remember. This essay follows and unravels extended interiors for how they operate shaping space and the human behaviors within those spaces.
Accurate Monocular Depth Estimation via Interaction of Hierarchical Features

Abstract Monocular depth estimation is a challenging task, which assists in understanding 3D scene geometry from the same 2D scene. The ordinal-regression-method demonstrates superior performance in this issue but naive ordinal inference strategy for inferring the final depth values and naive operations to up-sample to the desired space scale introduce significant discretization errors and object boundary confusion. Firstly, we come up with a novel inference strategy to reduce the discretization errors. And then a specifically designed decoder that completes the fusion of different hierarchical features under guidance and the fusion feature reconstruction. We evaluate on a public monocular depth-estimation benchmark dataset (NYU Depth V2). The experimental results show that the method proposed outperforms other ordinal regression methods.
Improvement of Semantic Segmentation with DC-GAN in Autonomous driving

Semantic segmentation is a long-standing challenge in computer vision. In the recent years, Generative Adversarial Network (GAN), has been turned to useful and common approach in data generation which can be used in deep learning problems. Adversarial training has been shown to produce efficient outcomes for generative image architecture. In current research, a generative adversarial framework is joint with a pre-trained deep semantic segmentation network as a post-processing unit to achieve better quality in the prediction masks. In the GAN, we attempt to culminate to minimum the loss between produced masks and ground truth masks. Our motivation is to generate masks which have the same distribution with ground truth yet more precise than output of semantic segmentation network. Hence, GAN is behaved as a sort of post-processing method for the semantic segmentation as a tricky problem to gain more high-quality throughput. Many of experiences are carried out on two open-source datasets, which include autonomous driving samples, show the efficiency of the recommended model. Our experiences demonstrate that our adversarial method propel to enhanced accuracy on the PASCAL VOC 2012 and Cityscape datasets.
Distributed Ledger Technology: Secuirty Threats in Cryptography

DLT has become an important topic of the world in recent years. Today Distributed Ledger Technology is growing famous among transactions and storing data between organizations. It has given an economic boom to many businesses and industries. DL technology brings a revolutionary change in maintaining secure transactions and storing data on a network of machines. The data stored use crypto technology to make it secure. Although this technology brings a revolutionary change and more convenient services, the security issues of crypto technology is also a very important discussion which we need to concern. This paper discusses the security threats, deficiencies and weaknesses of Crypto Technology. Security issues is discussed which might occurred in systems, networks, transaction etc.
Some Ethical and Moral Aspects of Decision Making in the Light of Research on Physical Aggression

The paper addresses factors that inform decision-making processes relating to physical aggression in the context of needs for decision support. The authors present the findings of research on reactions to physical aggression declared by survey respondents when confronted with a physical assault targeted at themselves on the one hand, and at others on the other. The survey attempted to inquire into what actions people will take when they are themselves subject to a physical assault, and what actions they will take when witnessing a physical assault on another person. Further, the research sought to investigate whether and to what extent the object of physical abuse (the respondents themselves or other people) determines the kind of action taken. The study involved 294 individuals (73 women and 221 men) that were selected randomly from amongst 1st year students and non-enrolled applicants of the Wrocław-based Military University of Land Forces. The findings lead to conclusions on the viability and applicability of computerised decision support, specifically through a hybrid decision support platform, for the identification and assessment of aggressive behaviours.
A Novel approach of Designing E-commerce authentication scheme using Hybrid Cryptography based on Simple symmetric key and extended Linear block cipher algorithm

Cryptography algorithm leads the secured data transaction and it act as crucial and significant role in present business environment and supports protection to transact business information over the Internet. Successful online business or internet business required reliability for online products and security for digital money transaction. Designing and implementing new algorithms are essential to lead the safe business transaction. In this research article, we proposed new hybrid cryptographic authentication scheme, combines two different symmetric algorithm based on simple integer variable and extended linear block cipher using modular 37. Proposed hybrid combination provides the opportunity to enhance e-commerce business with basic primitives of cryptography's integrity, confidentiality and authenticity.
Critical sociocognitive analysis of hate speech in the 2015 Nigerian presidential election campaigns

Abstract In an attempt to ensure electoral victory, politicians use different strategies. One of such is resort to hate speech to discredit the opponents. The 2015 presidential election campaigns in Nigeria witnessed unrestrained use of hate speech in different media. This paper analysed selected speeches of the campaign organisations of the two leading political parties in the election, namely the Peoples Democratic Party Presidential Campaign Organisation and the All Progressives Congress Presidential Campaign Organisation. Data were got from the speeches and advertorials of the two organisations sampled from The Nation and The Punch , published between January and March 2015. The speeches were subjected to critical sociocognitive analysis. The antecedents of the candidates became a recipe for hate speech. Propagandist and alarmist ideologies were used. Socially shared knowledge played an important role in the hate speech. Figures were used symbolically and serious attempts were made to frighten the electorate.
Design and Implementation of Financial Information Management System for Mobile Terminal

This paper designs the detailed scheme of the mobile financial information management system, including the design of software interface, user experience process design, server protocol interface design and client local database. The implementation of server-side is based on MVC mode in J2EE, using SSH framework, Java language and lightweight database SQLite. Each framework is responsible for the corresponding level of functions, and the connection between the upper and lower layers only depends on the interface. The client is mainly based on Android platform technology, which will divide the client into information interaction unit and data calculation unit according to the actual needs of the client, and complete the function by interacting with the server. The interaction of the system mainly adopts UI service mode, through the service-oriented application and the unified interface center, to establish the intelligent service of multi-terminal application interconnection and sharing. The test results of the system verify that all aspects of the scheme in this paper perform well and it can be further used widely.
Promising Bio-Authentication Scheme to Protect Documents for E2E S2S in IoT-Cloud

Document integrity and origin for E2E S2S in IoTcloud have recently received considerable attention because of their importance in the real-world fields. Maintaining integrity could protect decisions made based on these message/image documents. Authentication and integrity solutions have been conducted to recognise or protect any modification in the exchange of documents between E2E S2S (smart-to-smart). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent known attacks or applicable to smart devices. We propose a robust scheme that aims to protect the integrity of documents for each users session by integrating HMAC-SHA-256, handwritten feature extraction using a local binary pattern, one-time random pixel sequence based on RC4 to randomly hide authentication codes using LSB. The proposed scheme can provide users with one-time bio-key, robust message anonymity and a disappearing authentication code that does not draw the attention of eavesdroppers. Thus, the scheme improves the data integrity for a users messages/image documents, phase key agreement, bio-key management and a one-time message/image document code for each users session. The concept of stego-anonymity is also introduced to provide additional security to cover a hashed value. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme.
Automatic correction system of elementary arithmetic images based on deep learning

Abstract The elementary arithmetic exercise is corrected by manual traditionally, which is time consuming and inefficient. With the development of machine learning technology, the accuracy of image recognition is gradually improved, which can assist the manual correcting task. In this paper, a convolutional neural network model is built and trained to realize the recognition of numbers and operators in images based on Keras framework. After the training process, an automatic correction system for elementary arithmetic images is developed using the Django framework. The trained model is used to identify and correct the elementary arithmetic images automatically. The user uploads the elementary arithmetic images by the browser without installing any other application software, which can be used to help teachers in practice after some optimizations.
WEB-BASED DECISION SUPPORT SYSTEM FOR GUEST STAR DETERMINATION IN E-MARKETPLACE USING WEIGHTED PRODUCT METHOD

At an event, the event organizer usually requires guest stars as performers. Event organizers often find it difficult to find a guest star that suits their needs for their event. The purpose of this research is to build an e-marketplace application as a web-based decision support system that can be used to find alternative decisions to choose guest stars according to the criteria using product weighting methods. The weighted product method is one of the methods that can be applied to web-based decision support system applications for determining guest stars in e-marketplaces. There are three criteria used to determine guest stars in this e-marketplace, these criteria are the type of show, price and location. As for the alternatives that were tested, there were nine alternatives consisting of guest stars in Yogyakarta. Functionality testing is carried out using the black box method and the result is that all functions and validation buttons on the e-marketplace can function properly. The result of calculation using the weighted product method is the guest star with the highest yield is Frengki Sermadi with a value of 0.135234, while the guest star with the lowest result is Sanggar Titik Dua with a value of 0.083222.
Speed regulation of DC motors based on on-line optimum asynchronous controller tuning and differential evolution

The use of bio-inspired algorithms into controller tuning is one of the main topics in the last decades. One crucial issue when using bio-inspired algorithms to on-line tune the control system (adaptive controller tuning) is the computational time. In this work, an Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the frequent activation of the tuning process, and hence, the computational cost could be reduced. This approach is based on a proposed event function, which determines the update time instant of the control parameters through the Differential Evolution algorithm. Comparative results with a Synchronous Adaptive Controller Tuning (SACT) approach are included in the study case of the speed regulation of the DC motor under the effects of uncertainties in the load. The SACT approach periodically updates the controller parameter at each sampling time. The comparative analysis indicates that the proposed AACT significantly reduces the controller parameter update without considerably increase the regulation error.
Cryptocurrencies asset pricing via machine learning: Extended abstract

In this paper, we study the cryptocurrency asset pricing via machine learning. Cryptocurrency is based on the blockchain technology. In 2008, a person who published an idea. By 2011, that idea is a market worth over 54 million. After bitcoin began taking off, hundreds of alternative cryptocurrencies formed an in-depth market system. More advanced, alternative forms of blockchains are springing up as autonomous corporations.
Usability Study of Data Entry Design Patterns for Mobile Applications

A study of usability testing of data entry methods in mobile applications was conducted. The motivation for this work was to find alternative ways of entering data that are characterized by high usability and to identify problems of currently used methods along with the proposals how to solve them. The following types of inputted data were chosen credit card details, date, time, small numbers and longer texts. In order to conduct the experiments a mobile application was implemented. It was divided into different versions depending on the input methods used. Each user tested one version of the application by executing predefined usage scenarios. The obtained results confirmed the great potential of data input methods that use voice recognition and text scanning. According to the conducted research, these methods achieve both good performance and satisfaction with users.