Reviewerly/exHarmony huggingface dataset:
Regularization theory in the study of generalization ability of a biological neural network model

This paper focuses on the generalization ability of a dendritic neuron model (a model of a simple neural network). The considered model is an extension of the Hodgkin-Huxley model. The Markov kinetic schemes have been used in the mathematical description of the model, while the Lagrange multipliers method has been applied to train the model. The generalization ability of the model is studied using a method known from the regularization theory, in which a regularizer is added to the neural network error function. The regularizers in the form of the sum of squared weights of the model (the penalty function), a linear differential operator related to the input-output mapping (the Tikhonov functional), and the square norm of the network curvature are applied in the study. The influence of the regularizers on the training process and its results are illustrated with the problem of noise reduction in images of electronic components. Several metrics are used to compare results obtained for different regularizers.
Social multi-modal event analysis via knowledge-based weighted topic model

Along with the development of mobile Internet and social network, people’s lifestyles are also changing, and many social websites (e.g. Facebook, YouTube, and WeChat) have sprung up, which leads to the emergence of a large number of multimedia data (e.g. text, image, and video) of various events. The goal of this paper is to mine event topics efficiently from massive and unordered social media data, which is beneficial to search, browse and monitor significant social events for users or governments. In order to achieve this goal, this paper proposes a novel Knowledge-Based Multi-Modal Weighted Topic Model (KBMMWTM) for social event analysis. The proposed KBMMWTM has following advantages: (1) The proposed KBMMWTM can effectively take advantage of the multi-modality of social events jointly. (2) The proposed KBMMWTM exploits word correlation in dataset as prior knowledge to improve the performance of event topic mining. We evaluate our KBMMWTM model on a real dataset and full experiments show that our model outperforms state-of-the-art models.
Chaining Property to Blocks – On the Economic Efficiency of Blockchain-Based Property Enforcement

Within the last two years, much has been written about the blockchain and distributed ledger technologies. However, few actual use cases related to real world phenomena have been proffered in that literature. Most applications remain entirely in the virtual world or their descriptions remain on a very abstract and speculative level. In this paper we study one possible application on the powers of blockchain technology to real-world problems. In particular, we study the economic feasibility, effectiveness and efficiency of blockchain-based registries for property of chattel and the technical enforcement of the rights listed in such registries. For the example of smartphones, we show that their, and their owners’ registration in a blockchain may achieve a most desirable results from registration, theft becomes less attractive. An additional advantage, which we also briefly touch on, is that the use of smartphones as collateral without possession may become possible. We study under what conditions the benefits from registration is feasible in a blockchain-based distributed ledger and why they are not implemented under less complex technologies such as registries owned and administered by producers of smartphones.
Evaluation of Fraud Examinations: The Case of Inappropriate Accounting Practices at Fuji Xerox

There is a growing business for global auditing firms and local law firms as well as independent detectives to conduct forensic investigations at client organizations when there is suspicion of white-collar misconduct and crime. Client organizations normally do not disclose investigation reports. An investigation report concerned with inappropriate accounting practices at Fuji Xerox in New Zealand is publicly available. This article presents an evaluation of the investigation. Specifically, this article focuses on the mandate, where examiners were asked to find causes of misconduct. Examiners present eight reasons for inappropriate accounting practices. However, as discussed in this evaluation, the report of investigation presents little or no evidence for most of the causes.
Completeness for μ-calculi: A coalgebraic approach

We set up a generic framework for proving completeness results for variants of the modal mu-calculus, using tools from coalgebraic modal logic. We illustrate the method by proving two new completeness results: for the graded mu-calculus (which is equivalent to monadic second-order logic on the class of unranked tree models), and for the monotone modal mu-calculus. Besides these main applications, our result covers the Kozen–Walukiewicz completeness theorem for the standard modal mu-calculus, as well as the linear-time mu-calculus and modal fixpoint logics on ranked trees. Completeness of the linear-time mu-calculus is known, but the proof we obtain here is different and places the result under a common roof with Walukiewicz' result. Our approach combines insights from the theory of automata operating on potentially infinite objects, with methods from the categorical framework of coalgebra as a general theory of state-based evolving systems. At the interface of these theories lies the notion of a coalgebraic modal one-step language. One of our main contributions here is the introduction of the novel concept of a disjunctive basis for a modal one-step language. Generalizing earlier work, our main general result states that in case a coalgebraic modal logic admits such a disjunctive basis, then soundness and completeness at the one-step level transfer to the level of the full coalgebraic modal mu-calculus.
Big Data Security and Privacy: New Proposed Model of Big Data with Secured MR Layer

The publication and dispersal of crude information are urgent components in business, scholarly, and restorative applications. With an expanding number of open stages, for example, informal communities and cell phones from which information might be gathered; the volume of such information have likewise expanded after some time progressed toward becoming as Big Data. The traditional model of Big Data does not specify any level for capturing the sensitivity of data both structured and combined. It additionally needs to incorporate the notion of privacy and security where the risk of exposing personal information is probabilistically minimized. This paper introduced security and privacy layer between HDFS and MR Layer (MapReduce) known as new proposed Secured MapReduce (SMR) Layer and this model is known as SMR model. The core benefit of this work is to promote data sharing for knowledge mining. This model creates a privacy and security guarantee and data utility for data miners. In this model, running time, CPU usage, Memory usage, and Information loss are less as compared to traditional approaches.
QoE Estimation Model for a Secure Real-Time Voice Communication System in the Cloud

As moving towards cloud-based real-time services, we are witnessing the shift from a technology-driven services to service provisioning paradigms, that is, from Quality of Service (QoS) to Quality of Experience (QoE). User experience and satisfaction are placed at the epicenter of the system design. QoE is a measurement of user experience on the provided service by a system. Often QoE is measured by subjective mechanisms, such as user experience surveys and mean opinion scores (MOS) methods, which can be a costly and time-consuming process. Using an adequate QoE model to measure user experience of perceived quality is cost-effective, compared to using time-consuming subjective surveys. Applying an adequate QoE model to assess user experience is advantageous for cloud-based real-time services such as voice and video. This study uses a formula-based QoE estimation model to estimate and predict QoE prior to the deployment or during the planning stage of the system service. This study investigates a real-world scenario of a company that recently moved to its premises-based real-time trading communication system (TCS) to a public cloud. A simulation system using OPNET is also implemented to illustrate the usefulness of the model. Our result shows that the effect of delay on the users experience of the service provided by the cloud-based TCS is minimum comparing to packet loss rate (PLR) and Jitter. However, it has been observed that the overhead of the different security settings of the TCS system had no major negative impact to the user experience. The proposed model can be used as a QoE control mechanism and network optimization for cloud-based TCS services.
Optimization of String Rewriting Operations for 3D Fractal Generation with Genetic Algorithms

String rewriting is a modification of the idea for the context-free grammar. Modification consists in the fact that there is not separation on terminal and nonterminal symbols. Each symbol in string rewriting, is considered as nonterminal and it can produce longer sequence. By this way infinite structures are created as fractals. In a video presentation by Jack Hodkinson the use of 2D images instead of text symbol pixels is suggested. Each pixel (geometric square) is divided in nine sub-squares. The color of the pixel determine the pattern in which the nine squares are arranged. By this way each pixel gives a rule for subdivision of the containing area. Hodkinson gives a method, to setup the fractal, to reproduce a particular 2D image (for example, the glyph of the Greek letter $$\pi $$ ). This problem is well known in the fractals theory as Fractal Inverse Problem (FIP). It is an optimization problem, so a good option is the use of genetic algorithm (GA), to assemble set of rules, for string rewriting with 2D pixels. Series of experiments with the size of substitution matrix (not only 3 $$\times \,$$ 3, but also 2 $$\times \,$$ 2 and 4 $$\times \,$$ 4) are done by Hodkinson. Also are made series of experiments with different number of colors, starting with black/white and reaching the final successful experiment with thirty shades of blue.
The Impact of Named Entity Translation for Neural Machine Translation

Named entity translation has been shown in many studies that could have positive impact on performance of sentence level neural machine translation. In this paper, we study a mainstream structure that incorporating an external named entity translation model to neural machine translation. We give several comparison experiments by applying different named entity translation model structures, to clearly represent the impact of this structure in improving quality of neural machine translation. The experiments show that the proposed approach is able to achieve posistive result on some datasets and we give our analysis of influence factors.
Visibilities and the Analysis of Interdiscourse: The Case of Digital Health Practices

This contribution examines the study of interdiscourses in visual discourse analysis. An expanded theory of interdiscursivity considers the integration, plausibility, and popularization of meanings, offering a perspective on ways of legitimating interventions and instructions or guidelines for action. Based on the reconstruction of meaning assignments, the expanded interdiscourse theory presented here opens up the possibility of tracing how power is exercised through the occupation of particular discourse positions, whether these are of a linguistic or of a pictorial/visual nature. With reference to poststructuralist theories, assignments of meaning are seen as inherently temporary and unstable. Whatever social reality is, it remains at least partially ambiguous and is determined anew in conflicts or tensions surrounding hegemonic negotiations. Visual discourse analysis thus focuses on the precarious nature of hegemony-building processes. How such adjustments of meaning through visualizations can be investigated within the framework of interdiscursive analysis is discussed using the example of the digitalization of body data via wearables.
Estimation for a Thrusting/Ballistic Object With Mass Ejection From a Single Fixed Passive Sensor With Delayed Acquisition

The trajectory estimation problem of a thrusting/ballistic object in three-dimensional (3-D) space has been previously solved with 2-D measurements (azimuth and elevation angles from a fixed passive sensor, either starting from the launch time or with a delayed acquisition) under the assumption of constant mass. However, since the mass decreases as the fuel burns, this should be accounted for. This paper investigates several approaches with different parameter vectors to solve the trajectory estimation and impact point prediction (IPP) with measurements starting after the launch time, i.e., delayed acquisition for both constant mass motion model and mass ejection motion model. For the mass ejection motion model, the mass ejection rate is an extra component of the parameter vector to be estimated. The invertibility of the Fisher information matrix (FIM) of the parameter vectors is also used to confirm the observability (estimability) of the system. The Cramer-Rao lower bound (CRLB) is the inverse of the FIM if it is invertible. The CRLB of the IPP is also derived. We develop the maximum likelihood estimator of the considered motion parameter vectors. Performance comparison between the models considered is given and the statistical efficiency of the best model is confirmed via simulation results.
The OpenUP Pilot on Research Data Sharing, Validation and Dissemination in Social Sciences

The paper presents the results of a pilot carried out within the European project OpenUp (Opening up new methods, indicators and tools for peer review, dissemination of research results and impact measurement). Aim of the pilot is to investigate the applicability of peer review and/or Open Peer Review (OPR) to datasets in disciplines related to Social sciences. Main emphasis is given to the characteristic and features of data sharing and validation in this heterogeneous scientific field, thus providing the basis for the selection of the community chosen for the pilot. Indications emerging from the analysis of the interviews carried out in the pilot can drive the adoption of data quality assessment, and hence peer review, as well as provide some principles that can incentivize other scientific communities to share their research data.
How Sensitivity Classification Effectiveness Impacts Reviewers in Technology-Assisted Sensitivity Review

All government documents that are released to the public must first be manually reviewed to identify and protect any sensitive information, e.g. confidential information. However, the unassisted manual sensitivity review of born-digital documents is not practical due to, for example, the volume of documents that are created. Previous work has shown that sensitivity classification can be effective for predicting if a document contains sensitive information. However, since all of the released documents must be manually reviewed, it is important to know if sensitivity classification can assist sensitivity reviewers in making their sensitivity judgements. Hence, in this paper, we conduct a digital sensitivity review user study, to investigate if the accuracy of sensitivity classification effects the number of documents that a reviewer correctly judges to be sensitive or not (reviewer accuracy) and the time that it takes to sensitivity review a document (reviewing speed). Our results show that providing reviewers with sensitivity classification predictions, from a classifier that achieves 0.7 Balanced Accuracy, results in a 38% increase in mean reviewer accuracy and an increase of 72% in mean reviewing speeds, compared to when reviewers are not provided with predictions. Overall, our findings demonstrate that sensitivity classification is a viable technology for assisting with the sensitivity review of born-digital government documents.
Emergency, Pictogram-Based Augmented Reality Medical Communicator Prototype Using Precise Eye-Tracking Technology

This article details the process involved in the development of a new and revolutionary solution for medical communication. Our solution precisely tracks head and eye movement and allows the user to interact with an intuitive, pictogram-based interface. Around the world, many people rely on written instruction and verbal or nonverbal communication to accompany medication or provide additional information on a patient's symptoms. However, poor language skills and a host of health problems, psychological disorders, and trauma can limit a person's ability to speak or move. Our solution could drastically improve the quality of life of patients suffering from brain injury, hearing loss, and many other communication disorders and disabilities. They can begin to communicate with greater ease and fluency, feel connected to the world, and experience better and more accurate medical care. The potential applications have many incredible advantages, ranging from improved communication with family to emergency medical uses that can save the life of an affected individual. The use of augmented reality in our solution, as well as the secondary camera to improve accuracy in eye tracking, makes this solution for more accuracy, whereas the inclusion of a pictogram-based interface makes it a practical solution that cuts down on the error margins of traditional accessibility devices and makes it equally accessible to children and speakers of foreign languages. According to the data gathered, we confirm that the solution we propose has many possible practical applications in improving the health, safety, and quality of life of the user.
Urdu language based information dissemination system for low-literate farmers

This paper describes the design process by which we designed an Android application equipped with audio, textual menus and visuals components for use by farmers of diverse literacy levels looking for vital weather information after the conclusion of research-work that productivity lags due to information inadequacies. The intervention provides more timely access to accurate information to low-literate farmers and thereby help in making the agricultural ecosystem more robust. We discuss the various design and implementation features of our system and presents our findings from the field on the usability of our application. We have also openly released our source code so that other users and developers can also benefit from our work.
Remixing entity linking evaluation datasets for focused benchmarking

In recent years, named entity linking (NEL) tools were primarily developed in terms of a general approach, whereas today numerous tools are focusing on specific domains such as e.g. the mapping of persons and organizations only, or the annotation of locations or events in microposts. However, the a vailable benchmark datasets necessary for the evaluation of NEL tools do not reflect this focalizing trend. We have analyzed the evaluation process applied in the NEL benchmarking framework GERBIL [in: Proceedings of the 24th International Conference on World Wide Web (WWW’15), International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 2015, pp. 1133–1143, Semantic Web 9(5) (2018), 605–625] and all its benchmark datasets. Based on these insights we have extended the GERBIL framework to enable a more fine grained evaluation and in depth analysis of the available benchmark datasets with respect to different emphases. This paper presents the implementation of an adaptive filter for arbitrary entities and customized benchmark creation as well as the automated determination of typical NEL benchmark dataset properties, such as the extent of content-related ambiguity and diversity. These properties are integrated on different levels, which also enables to tailor customized new datasets out of the existing ones by remixing documents based on desired emphases. Besides a new system library to enrich provided NIF [in: International Semantic Web Conference (ISWC’13), Lecture Notes in Computer Science, Vol. 8219, Springer, Berlin, Heidelberg, 2013, pp. 98–113] datasets with statistical information, best practices for dataset remixing are presented, and an in depth analysis of the performance of entity linking systems on special focus datasets is presented.
Receipt-Free, Universally and Individually Verifiable Poll Attendance

We examine cryptographic techniques for protecting voters from coercion not to vote in pollsite elections. Although many other works have evaluated the receipt-freeness of the ballots cast by voters, very few have considered the privacy issues arising from whether the voter attended the polling place at all. Our objectives are simple: to simultaneously protect against voter coercion and ballot stuffing. In our voter attendance verification scheme, any voter can verify whether their attendance has been counted (which indicates whether they voted or not), and any third party can verify the total number of voters that attended any given polling place on election day. To mitigate voter coercion, impersonation and disenffanchisement, our scheme requires the look-up of a public web bulletin board, but it does not require complex voter verification -- we simply expect the voters to check the consistency of the 1-bit secret integer that was assigned to them during registration. We prove the receipt-freeness property of our scheme to ensure that a voter (or a registered non-voter) when challenged by an adversary, can always produce a transcript that indicates either that they have attended, or if they prefer, that they have not attended their assigned polling place to vote.
Social Media Best Practices

Large, decentralized academic libraries often lack standardization or cohesiveness across social media accounts and platforms. While subject libraries need to maintain content and tone appropriate for their audience and platforms, establishing guidelines and best practices will facilitate management of social media accounts and help to ensure a standard of accessibility across all library accounts. This chapter will present a case study of a very large academic library collaboratively putting together a set of guidelines specifically about copyright and disability issues on social media platforms such as Twitter, Facebook, and Instagram. It will also discuss disseminating these guidelines through a series of workshops targeted at social media managers, and will offer an initial assessment of the guidelines’ impact on social media account owners, managers, and users.