Reviewerly/exHarmony huggingface dataset:
Continuous-Time Deconvolutional Regression for Psycholinguistic Modeling

The influence of stimuli in psycholinguistic experiments diffuses across time because the human response to language is not instantaneous. The linear models typically used to analyze psycholinguistic data are unable to account for this phenomenon due to strong temporal independence assumptions, while existing deconvolutional methods for estimating diffuse temporal structure model time discretely and therefore cannot be directly applied to natural language stimuli where events (words) have variable duration. In light of evidence that continuous-time deconvolutional regression (CDR) can address these issues (Shain &amp;amp; Schuler, 2018), this article motivates the use of CDR for many experimental settings, exposits some of its mathematical properties, and empirically evaluates the influence of various experimental confounds (noise, multicollinearity, and impulse response misspecification), hyperparameter settings, and response types (behavioral and fMRI). Results show that CDR (1) yields highly consistent estimates across a variety of hyperparameter configurations, (2) faithfully recovers the data-generating model on synthetic data, even under adverse training conditions, and (3) outperforms widely-used statistical approaches when applied to naturalistic reading and fMRI data. In addition, procedures for testing scientific hypotheses using CDR are defined and demonstrated, and empirically-motivated best-practices for CDR modeling are proposed. Results support the use of CDR for analyzing psycholinguistic time series, especially in a naturalistic experimental paradigm.
Overspecification and incremental referential processing: An eye-tracking study

Using eye-tracking, we examined if over-specification hinders or facilitates referent selection, and the extent to which this depends on the properties of the attribute mentioned in the referring expressions and the underpinning processing mode. Following spoken instructions, participants selected the referent in a visual display while their eye movements were monitored. The referring expressions were presented either simultaneously with the displays, so the attributes could be incrementally processed in sequence, or before the display presentation, so the attributes could be processed in parallel from the outset of search. Experiment 1 showed that when the attributes were processed incrementally, how quickly an earlier-mentioned attribute discriminated determined whether a late-mentioned, over-specified attribute contributed to discrimination: When color was mentioned first and is fully discriminating, the referent was selected fast regardless of the second-mentioned pattern, whereas when pattern was mentioned first and is fully discriminating, the second-mentioned color facilitated discrimination. Experiment 2 found that under incremental processing, color mention after a fully discriminating pattern increased fixations but delayed referent selection relative to a pattern-only description; under parallel processing, however, color mention immediately eliminated alternatives and sped up referent selection. Experiment 3 showed that pattern mention after a fully discriminating color delayed referent selection and tended to reduce fixations relative to a color-only description in both processing modes. Hence, additional attributes can speed up referent selection but only when they can discriminate much faster than alternative attributes mentioned in a more concise description, and critically, when they can be used early for referent search.
An Exploratory Study in SME’s for Software Defect Prediction

Software defect prediction is a process of constructing machine learning classifiers to predict defective code snippets, using historical information in software repositories such as code complexity and change records to design software defect metrics , In this research article we have tried to understand the relationships between various variables which are important for IT SME’s ,The study is carried out with the help of a well structured questionnaire using IBM SPSS tool for data analysis and interpretation.
Webinar in Support of Steam Training in Class

A two-subjects webinar: information technology and arts with the participation of a lecturer from the Institute of Mathematics and Informatics and the Laboratory of Telematics of the Bulgarian Academy of Sciences is presented. The article describes the concrete activities of the e-facilitator, the lecturer and the teachers in this specific scenario. In order to make it easier for the e-facilitator, a "webinar technical readiness check" form is prepared. Inside are listed possible difficulties when hosting the webinar during a class along with possible solutions.
Adoption of Blockchain in Health Care (Preprint)

<sec> <title>UNSTRUCTURED</title> This study aims to review current issues regarding the application of blockchain technology in health care. We illustrated the various ways in which blockchain can solve current health care issues in three main arenas: data exchange, contracts, and supply chain management. This paper presents several current and projected uses of blockchain technology in the health care industry. We predicted which of these applications are likely to be adopted quickly and provided a supply chain example of tracking the transportation of organs for transplantation. </sec>
Comparative Study for Different Provisioning Policies for Load Balancing in Cloudsim

Distributing application requests across applications located in different datacenters with in cloud equally must be provided by cloud load balancing. In this paper, we compare different provisioning policies within cloud for virtual machines and workloads, where we are focusing on how to distribute the processing power between virtual machines and how to distribute workload among virtual machines. Cloudsim is the simulation plate form used to test the different distributions scenarios to check the performance on makespan, average turnaround time, bandwidth utilization and CPU utilization. Result showed the difference in performance between the three tested provisioning schemes, where the space-shared gives better readings for the selected performance metrics.
Automatic Annotation of Events and Highlights Generation of Cricket Match Videos

In this paper, we propose a novel method for automatic annotation of events and highlights generation for cricket match videos. The videos are divided into time intervals representing one ball clip using a Convolutional Neural Network (CNN) and Optical Character Recognition (OCR). CNN detects the start frame of a ball. OCR is used to detect the end of the ball and to annotate it by recognizing the change in runs/wickets. The proposed framework is able to annotate events and generate sufficiently good highlights for four full length cricket matches.
State of the Art in Adoption of Contact Tracing Apps and Recommendations Regarding Privacy Protection and Public Health: Systematic Review (Preprint)

<sec> <title>BACKGROUND</title> During the COVID-19 pandemic, contact tracing apps have received a lot of public attention. The ongoing debate highlights the challenges of the adoption of data-driven innovation. We reflect on how to ensure an appropriate level of protection of individual data and how to maximize public health benefits that can be derived from the collected data. </sec> <sec> <title>OBJECTIVE</title> The aim of the study was to analyze available COVID-19 contact tracing apps and verify to what extent public health interests and data privacy standards can be fulfilled simultaneously in the process of the adoption of digital health technologies. </sec> <sec> <title>METHODS</title> A systematic review of PubMed and MEDLINE databases, as well as grey literature, was performed to identify available contact tracing apps. Two checklists were developed to evaluate (1) the apps’ compliance with data privacy standards and (2) their fulfillment of public health interests. Based on both checklists, a scorecard with a selected set of minimum requirements was created with the goal of estimating whether the balance between the objective of data privacy and public health interests can be achieved in order to ensure the broad adoption of digital technologies. </sec> <sec> <title>RESULTS</title> Overall, 21 contact tracing apps were reviewed. In total, 11 criteria were defined to assess the usefulness of each digital technology for public health interests. The most frequently installed features related to contact alerting and governmental accountability. The least frequently installed feature was the availability of a system of medical or organizational support. Only 1 app out of 21 (5%) provided a threshold for the population coverage needed for the digital solution to be effective. In total, 12 criteria were used to assess the compliance of contact tracing apps with data privacy regulations. Explicit user consent, voluntary use, and anonymization techniques were among the most frequently fulfilled criteria. The least often implemented criteria were provisions of information about personal data breaches and data gathered from children. The balance between standards of data protection and public health benefits was achieved best by the COVIDSafe app and worst by the Alipay Health Code app. </sec> <sec> <title>CONCLUSIONS</title> Contact tracing apps with high levels of compliance with standards of data privacy tend to fulfill public health interests to a limited extent. Simultaneously, digital technologies with a lower level of data privacy protection allow for the collection of more data. Overall, this review shows that a consistent number of apps appear to comply with standards of data privacy, while their usefulness from a public health perspective can still be maximized. </sec>
THE EMERGENCE OF CYBER LITERATURE: A CHALLENGE TO TEACH LITERATURE FROM TEXT TO HYPERTEXT

In a digital era, people live in a cyberspace that they become part of modern society. The information that they have acquired is from the World Wide Web (WWW). This WWW has become an important medium for people in the world to disseminate information. Because of the technology of Web, cyber literature emerges. This study talks about the emergence of cyber literature which changes the way of reading and teaching in variousinstitutions. It becomes a challenge for people who teach literature because they should leave the printed text and move to the digital text as called hypertext. The existence of cyber literature also drives them change their style to analyze and criticize the work of literature. So, it becomes a challenge for them to teach literature from text to hypertext.
A Robust Watermarking Technique for Copyright Protection for Relational Databases

Internet based digitization has been of rapid increase in the usage of database applications at an enormous rate in recent times. It is very difficult to secure the ownership of digital assets because all the data on the internet are available everywhere almost free of cost and anybody can access such data and claim their ownership. In current digital scenario not only images, videos, and audio are in digital form. Databases are also digitized in different models and used as a service in database applications, including areas such as finance, multimedia, personnel, etc. A huge amount of confidential and sensitive data which are available publically facing a variety of threats like illegal copying, illegal redistribution, tampering, forgery and authentication. Authenticity, integrity, confidentiality and copyright protection are most important security issues to be addressed with most importance. Copyright management is a serious issue in database applications because it is much easier for others to download and manipulate copyrighted databases from the Internet and later re-use without any control. In this paper we proposed a robust watermarking scheme for copyright protection for relational databases, which protects the copy right information of the database even if the attacker tampers the data by changing the attribute values or reordering the tuples of the database. The proposed watermarking technique is robust since the watermark will not be lost even though the attacker tampers the data. The experiments show that the new method is efficient as well as effective for maintaining copy right information there by ensuring right protection to relational databases.
Machine Fault

Applying machine learning to subtle acoustic signals from an earthquake machine has revealed big clues about fault behavior in the lab.
Collecting Things Together: Sets

In this chapter we introduce the student to the world of sets. Actually, only a little bit of it, enough to get going. After giving a rough intuitive idea of what sets are, we present the basic relations between them: inclusion, identity, proper inclusion, and exclusion. We describe two common ways of identifying sets, and look closely at the empty set, basic Boolean operations, generalized meet and union, and powersets.
Modularizing Laravel

In the previous chapter, I mentioned that we had hit a sort of roadblock in our development of the claims processing application example we’ve been slowly defining since the beginning of the book. The application we are building has a number of different bounded contexts, a generic subdomain, and a bunch of different modules that encapsulate the various features and components that we need in our application. If we were to use the standard directory structure provided out of the box with a fresh Laravel installation, we would soon find things cluttered, scattered, and almost impossible to maintain once we started building even more complexity into the application when we implement future feature requests.
A Mathematical Model for Identifying Truth in Observations Made within Individual Human Self-Awareness

A large body of work purports to identify the functions of the human system through observations that can be validated within human self-awareness. That is, using observations that can be made in the human self-awareness to validate what in this paper are called human-centric models of the functionality of the human system, or human-centric functional models. This body of work, accumulated over thousands of years, has been largely inaccessible to the scientific study of consciousness and other functions because it's written in terms that require a very different training, which is training in observing one's self-awareness. Making scientific or mathematical use of observations made within human self-awareness is a challenge. What can be observed to be true in one's self-awareness, and what can be proved true mathematically, have so far been two different things. Representing an experiment as a channel through which information representing truth can be transmitted, this paper explores the use of Information Theory to gauge the capacity of an experiment in self-awareness to identify a truth, such as determining the validity of any given function defined by a functional model for consciousness. In providing this mechanism to prove that an awareness exists and that it reflects truth, this paper attempts to make the entire set of observations made using human self-awareness accessible to mathematicians and scientists.
Anti-Martingale Proximal Policy Optimization

Since the sample data after one exploration process can only be used to update network parameters once in on-policy deep reinforcement learning (DRL), a high sample efficiency is necessary to accelerate the training process of on-policy DRL. In the proposed method, a submartingale criterion is proposed on the basis of the equivalence relationship between the optimal policy and martingale, and then an advanced value iteration (AVI) method is proposed to conduct value iteration with a high accuracy. Based on this foundation, an anti-martingale (AM) reinforcement learning framework is established to efficiently select the sample data that is conducive to policy optimization. In succession, an AM proximal policy optimization (AMPPO) method, which combines the AM framework with proximal policy optimization (PPO), is proposed to reasonably accelerate the updating process of state value that satisfies the submartingale criterion. Experimental results on the Mujoco platform show that AMPPO can achieve better performance than several state-of-the-art comparative DRL methods.
Using Rule Induction to Assist in Rule Construction for a Natural-Language Based Intelligent Tutoring System

We used the Quinlan’s C4.5 machine learning algorithm to analyze tutorial dialogues as part of the derivation of planning rules for CIRCSIM-Tutor v. 3, a natural-language based intelligent tutoring system. We annotated a corpus of tutoring dialogues with an SGML-based representation of tutorial goals in order to make mechanical processing possible. We looked for rules of the form “under what conditions is goal x implemented with plan y?”. We discovered rules for high-level planning of the tutoring session and dynamic modification of the tutorial agenda. At a lower level of planning, we looked at rules for generating sections of the tutor’s utterance. The use of the rule induction algorithm has helped us discover which knowledge available to the planner is significant in making these decisions, as well as producing some decision trees we can actually use in CIRCSIM-Tutor.
A blockchain-based platform for smart contracts and intellectual property protection for the additive manufacturing industry

Autonomous machine-to-machine interaction with minimal human interference is one of the defining features of cyber-physical systems that are expected to become ubiquitous as the manufacturing industry enters the fourth industrial revolution. Blockchain technology could emerge as a technology that underpins cyber-physical systems because of its various desirable characteristics. This paper describes the design and implementation details of two components of a blockchain-based data model for the additive manufacturing industry. A smart contract framework is proposed for the creation and execution of agreements between design companies and 3D printing companies. It facilitates the verification and automatic enforcement of these contracts. The framework incorporates a dedicated platform for the management of design files for intellectual property (IP) protection-related use cases. This helps to control the usage and distribution of IP, which is a crucial requirement for a data management solution for the industry. The proposed platforms have been implemented using the Ethereum software stack and its advantages and use cases have been explored.
A collaboration strategy in the mining pool for proof-of-neural-architecture consensus

In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload. Thus, the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning. The mining pool manager partitions the full searching space into subspaces, and all miners are scheduled to collaborate on the Neural architecture search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than that of an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure completion of the tasks of high reward miners.
Exploring Widevine for Fun and Profit

For years, Digital Right Management (DRM) systems have been used as the go-to solution for media content protection against piracy. With the growing consumption of content using Over-the-Top platforms, such as Netflix or Prime Video, DRMs have been deployed on numerous devices considered as potential hostile environments. In this paper, we focus on the most widespread solution, the closed-source Widevine DRM.Installed on billions of devices, Widevine relies on crypto-graphic operations to protect content. Our work presents a study of Widevine internals on Android, mapping its distinct components and bringing out its different cryptographic keys involved in content decryption. We provide a structural view of Widevine as a protocol with its complete key ladder. Based on our insights, we develop WideXtractor, a tool based on Frida to trace Widevine function calls and intercept messages for inspection. Using this tool, we analyze Netflix usage of Widevine as a proof-of-concept, and raised privacy concerns on user-tracking. In addition, we leverage our knowledge to bypass the obfuscation of Android Widevine software-only version, namely L3, and recover its Root-of-Trust.
Meta‐feature based few‐shot Siamese learning for Urdu optical character recognition

Abstract Standard convolution neural network (CNN) achieves high level of accuracy for the recognition of characters in different languages. However, like other deep neural networks, training of CNN requires a substantial amount of data. Lack of sufficient training data invokes dataset bias, during learning process, which leads to a decay in the performance of CNN. The limitation of training data can be addressed by using few‐shot learners. In this research, CNN‐based few‐shot Siamese learner is trained on meta‐features, extracted from Urdu text images using a novel graph‐based normal to tangent line (GNTL) technique, for Urdu optical character recognition (OCR) across different font sizes. The learner is trained on three corpora (datasets) including one benchmark corpus “Centre for Language Engineering Text Images” and two other corpora, that is, “Urdu Thickness Graphs” (UTG) and “Urdu OCR Font 16 to 36” (UOF) which are developed and released in this research. 80% of data is used for training while 20% of data is used for testing. To create UTG corpus, the proposed novel feature extraction technique GNTL is used and a meta‐features‐based corpus is developed in form of thickness graphs. The third corpus UOF is based on five different font sizes, that is, 16, 20, 26, 30, and 36. The performance of few‐shot Siamese learner is compared with a standard CNN, trained on the same three corpora. Meta‐feature based few‐shot Siamese learner achieves a promising recognition accuracy and outperforms standard CNN by around 3%. On average, the performance of few‐shot Siamese learner is 96.82% while standard CNN reveals an average performance of 93.96%.