Reviewerly/exHarmony huggingface dataset:
Journalistic confidentiality in an age of data surveillance

This article examines two key components of the Australian Government’s data surveillance framework and critiques their impact on journalistic confidentiality. The 2015 mandatory data retention scheme and the 2018 telecommunications industry assistance scheme have been the subjects of considerable controversy and ongoing parliamentary reviews. The combined effect of these provisions is that journalists are unable to confidently fulfil their ethical obligation to maintain source confidentiality. The article recommends targeted reforms to more explicitly and appropriately balance the public interest in journalistic confidentiality (and relatedly, democracy, public accountability and the rule of law) against law enforcement and national security objectives.
A Comparative Study on Performance and Resource Utilization of Real-time Distributed Messaging Systems for Big Data

In the past few years, data continuously increase and in various forms called Big Data. Besides, data analytics play an important role more and more in most organizations. From these reasons, many efficiently distributed messaging systems have been introducing to handle Big Data in real-time. However, choosing the appropriate and efficient methods and tools to transfer Big Data is still challenging. Therefore, this paper purposes of comparing the architecture, performance, and resource utilization between Apache Kafka which is one of the favorite tools for Big Data and Apache Pulsar which is similar to Kafka and become one of the latest tools for big data. After we implemented both systems in the same environment, the results show that Pulsar outperforms in throughput, latency, and average resource utilization especially when the size of messages is small (such as 1 KB and 1MB).
Extreme Tensoring for Low-Memory Preconditioning

State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption. This has created a recent demand for memory-efficient optimizers. To this end, we investigate the limits and performance tradeoffs of memory-efficient adaptively preconditioned gradient methods. We propose \emph{extreme tensoring} for high-dimensional stochastic optimization, showing that an optimizer needs very little memory to benefit from adaptive preconditioning. Our technique applies to arbitrary models (not necessarily with tensor-shaped parameters), and is accompanied by regret and convergence guarantees, which shed light on the tradeoffs between preconditioner quality and expressivity. On a large-scale NLP model, we reduce the optimizer memory overhead by three orders of magnitude, without degrading performance.
Expert system for disease diagnosis in cocoa plant using android-based forward chaining method

Abstract Cocoa is one of the plantation crops that has high economic value besides it can also provide a large contribution so that it can increase the source of foreign exchange in Indonesia. This expert system is made for Android device users. In 2010 Indonesia succeeded in occupying the number three position after Ghana and Ivory Coast as the largest cocoa producer in the world. But the amount of cocoa production in West Sumatra has decreased due to a lack of knowledge of farmers in caring for and cultivating it. This is the background of this research, where this expert system uses the Forward Chaining method as a symptom search method that has been included as a tracking algorithm for each rule that has been set. So that it can produce an expert system for diagnosing cocoa plants that are right for farmers. When conducting a consultation, the user can answer Yes or No from the question given by the system. User answers are then processed based on rules and calculated using the forward chaining method. Testing is done to see if the system can run well as expected.
Computer-Assisted Use of Reading-through-Writing Method in Relation to Technical Literacy and Reading Motivation

The purpose of this study was to investigate how computer-assisted use of Reading-through-Writing (RtW) method relates to the level of literacy and reading motivation of school children in grades 1 and 2 (N = 261–385). The research also explored the extent, to which the RtW method was perceived as useful among teachers and by school children with lower reading and writing skills. The research was implemented as a quasi-experimental two-year follow-up study. The experimental group (N = 34–35) was taught by applying the RtW method, as defined by Trageton (2007). The control data (N = 227–350) were sourced from the first phase (2006–2011) of the First Steps Study: Interaction and Learning within Child-Parent-Teacher Triangle.  This research employed the embedded mixed methods research design. The quantitative data were obtained from 7 literacy tests and 3 reading motivation tests. The qualitative data were gathered from the study diaries filled by the teachers (N = 3), the interviews carried out with the school children (f = 19) and videos recorded during the lessons (f = 6). The results of this study have shown that the use of RtW method enriched the children’s experience of learning to read and write, although it did not have significant enhancing effect on technical literacy or reading motivation in the experimental group.
An Analysis of How Driver Experience Affects Eye-Gaze Behavior for Robotic Wheelchair Operation

Drivers obtain information on surrounding environment using their eyesights. Experienced eye-gaze behavior is needed when driving at places where multiple risks exist to prepare for and avoid them. In this work, we analyze the change in eye-gaze behavior in such situations while a driver gains experience on the operation of a robotic wheelchair. Accurate distance information in the traffic environment is important to analyze the eye-gaze behavior. However, almost all previous works analyze eye-gaze behavior in a 2D environment, so they could not obtain accurate distance information. For this reason, we analyze eye-gaze behavior in 3D space. Concretely, we developed a novel eye-gaze behavior analysis platform based on a robotic wheelchair and estimated the driver's attention in 3D space. We try to analyze the eye-gaze behavior considering a useful field-of-view in 3D space based on the distance information instead of only the fixation point to investigate the objects that a driver implicitly pays attention to and from where s/he focuses on them. Results show that novice drivers pay attention to a single risk at a time. In contrast, they pay more attention to multiple risks simultaneously as they gain experience. Additionally, we discuss what features are effective to model the eye-gaze behavior based on the results.
Mechanics-based model for the cooking-induced deformation of spaghetti

A strand of spaghetti changes shape as it is cooked. This paper proposes a theory that includes a time-dependent curvature, and models the evolution of the system based on previous observations. The authors compare their results with tabletop experiments and find good agreement.
Speed-up coherent Ising machine with a squeezed feedback system

As a solver for non-deterministic polynomial time (NP)-hard combinatorial optimization problems, the coherent Ising machine (CIM) is in the early stages of research, and the potential of this innovative physical system will be developed. Here, we propose a speed-up coherent Ising machine with a squeezed feedback system, which we call S-CIM. We couple squeezed feedback pulses generated by the squeezed feedback system into the degenerate optical parametric oscillator (DOPO) network. Simulations indicate that quantum inseparability of the coupled DOPO network is further enhanced during the whole optimization process, and quantum fluctuations are significantly smaller around the oscillation threshold. Computation experiments are performed on MAX-CUT problems of order between 4 and 20000. Numerical results demonstrate that S-CIM increases the optimal normalized output by 2.27% and significantly reduces the optimal computation time by 75.12%.
An Exact Reformulation of Feature-Vector-Based Radial-Basis-Function Networks for Graph-Based Observations

Radial basis function (RBF) networks are traditionally defined for sets of vector-based observations. In this brief, we reformulate such networks so that they can be applied to adjacency-matrix representations of weighted, directed graphs that represent the relationships between object pairs. We restate the sum-of-squares objective function so that it is purely dependent on entries from the adjacency matrix. From this objective function, we derive a gradient descent update for the network weights. We also derive a gradient update that simulates the repositioning of the radial basis prototypes and changes in the radial basis prototype parameters. An important property of our radial basis function networks is that they are guaranteed to yield the same responses as conventional radial basis networks trained on a corresponding vector realization of the relationships encoded by the adjacency matrix. Such a vector realization only needs to provably exist for this property to hold, which occurs whenever the relationships correspond to distances from some arbitrary metric applied to a latent set of vectors. We, therefore, completely avoid needing to actually construct vectorial realizations via multidimensional scaling, which ensures that the underlying relationships are totally preserved.
Attribute-Based Access Control in Service Mesh

Modern cloud applications can consist of hundreds of services with thousands of instances. In order to solve the problems of interservice interaction in this highly dynamic environment, an additional software infrastructure layer called service mesh is introduced. This layer provides a single point of interaction with the network for each service. Service mesh mechanisms are responsible for: load balancing, processing of network requests, service discovery, authentication, authorization, etc. However, the following questions arise: complex key management, fine-grained access control at the application level, confidentiality of data and many-to-many communications. It is possible to solve these problems with Attribute-based encryption (ABE) methods. This paper presents an abstract model of a service mesh and a protocol for interservice communications, which uses ABE for authorization and confidentiality of the messages.
Application of biometric models of authentication in mobile equipment

Today’s mobile devices are the most common investment in the technology industry as it is almost impossible to find a person who does not own a personal phone. Being the weakest point of nearly every user, as the data stored in it is essential, increased phone access security has been achieved by using biometric authentication modalities on these mobile devices. Here, the biometric modules that are most commonly used for authentication and how they are shared, the process of access and the weaknesses of each of these biometric modalities will be elaborated. Also indicate what are the potential attacks and problems that can be encountered in each of the biometric authentication methods on mobile devices.
Learning from Positive and Unlabeled Data without Explicit Estimation of Class Prior

Learning a classifier from positive and unlabeled data may occur in various applications. It differs from the standard classification problems by the absence of labeled negative examples in the training set. So far, two main strategies have typically been used for this issue: the likely negative examplesbased strategy and the class prior-based strategy, in which the likely negative examples or the class prior is required to be obtained in a preprocessing step. In this paper, a new strategy based on the Bhattacharyya coefficient is put forward, which formalizes this learning problem as an optimization problem and does not need a preprocessing step. We first show that with the given positive class conditional probability density function (PDF) and the mixture PDF of both the positive class and the negative class, the class prior can be estimated by minimizing the Bhattacharyya coefficient of the positive class with respect to the negative class. We then show how to use this result in an implicit mixture model of restricted Boltzmann machines to estimate the positive class conditional PDF and the negative class conditional PDF directly to obtain a classifier without the explicit estimation of the class prior. Many experiments on real and synthetic datasets illustrated the superiority of the proposed approach.
Proactive Building Energy Management Methods based on Fuzzy Logic and Expert Intelligence

Buildings consume a signifcant percentage of the world’s energy resources. The rapid depletion of energy resources, has imparted researchers to focus on energy conservation and wastage. The next generation of intelligent buildings is becoming a trend to cope with the needs of energy and environmental ease in buildings. This advances the intelligent control of building to fulfll the occupants’ need. Intelligent system control for sustainable buildings is dynamic and highly complex.
Improved Detection for WAMI using Background Contextual Information

Current vehicle detection and tracking in imagery characterised by large ground coverage, low resolution and low frame rate data, such as Wide Area Motion Imagery (WAMI), does not reliably sustain vehicle tracks through start-stop movement profiles. This limits the continuity of tracks and its usefulness in higher level analysis such as pattern of behaviour or activity analysis. We develop and implement a two-step registration method to create well-registered images which are used to generate a novel low-noise representation of the static background context which is fed into our Context Convolutional Neural Network (C-CNN) detector. This network is unique as the C-CCN learns changing features in the scene and thus produces reliable, sustained vehicle detection independent of motion. A quantitative evaluation against WAMI imagery is presented for a Region of Interest (ROI) of the WPAFB 2009 annotated dataset [1]. We apply a Kalman filter tracker with WAMI-specific adaptions to the single frame C-CNN detections, and evaluate the results with respect to the tracking ground truth. We show improved detection and sustained tracking in WAMI using static background contextual information and reliably detect all vehicles that move, including vehicles that become stationary for short periods of time as they move through stop-start manoeuvres.
Predicting Driver Lane Change Maneuvers Using Driver's Face

In this paper, we will present our project concerning realizing a system of predicting maneuvers before the vehicle turns through using a dataset containing videos of drivers in different situations and different maneuvers, as well as information on the environment, such as speed, empty lines and the existence of an artifact. After preparing the dataset, we built our CNN-LSTM model based on convolutional and recurrent layers. Our CNN-LSTM model allowed us to predict the maneuver with an accuracy of 94.1% and 3.75 seconds before the turn. Finally, For our model to be robust, we tried to detect anomalies and replace them with more meaningful values. We also tested our model by adding noise to the images.
Utilizing Public Blockchains for Censorship-Circumvention and IoT Communication

The advancement of blockchain technology and the Internet of Things (IoT) has presented us with unlimited possibilities to integrate the physical and the virtual worlds. In this work, we demonstrate how to override any existing public blockchain, that has enough redundancy in its transactions, to covertly broadcast arbitrary information. Besides, we implement and demonstrate our technique on a real-world cryptocurrency and show how it can be utilized in two specific applications: recording IoT data, and circumventing censorship.
Influence of digital learning orientation and readiness for change on innovative work behaviour: reflections from the higher education sector

Purpose The paper aims to examine the influence of students’ digital learning orientation on their readiness for change and innovative work behaviour. Elaborations are made on how these concepts can be utilized for strengthening the teaching-learning process in higher education institutions, and help them gain more cutting-edge competencies in areas of learning delivery and learning engagement. Design/methodology/approach The respondents for this descriptive study were drawn through random sampling, from an end-semester student group, who had taken up post-graduate courses in science and technology streams of a prominent science and technology university. Data was collected by administering self-reporting questionnaires. Findings The study revealed that higher digital learning orientation is associated with improved attitude towards change and higher innovative behaviour. With conflicting results in exiting literature, regarding the influence of digital learning orientation on learning outcomes, this study adds to the body of knowledge by testing previously unexplored propositions in a student sample. Originality/value This study is pioneering in conceptualizing and testing the proposed model. The paper emphasizes the role of digital learning orientation, especially in the context of today’s students being referred to as ‘digital natives’. It is thus imperative to understand how the same can be translated into learning outcomes. The results of the study highlights the need for augmenting the role of digital orientation in the teaching-learning process, so as to transform educational institutions sustainable in producing graduates with readiness for change and innovative work behaviour, in the context of an emergent digital economy.
On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence

ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Al-Imam A, Motyka MA. On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence. Alcoholism and Drug Addiction/Alkoholizm i Narkomania. 2019;32(3):237-242. doi:10.5114/ain.2019.91004. APA Al-Imam, A., & Motyka, M. A. (2019). On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence. Alcoholism and Drug Addiction/Alkoholizm i Narkomania, 32(3), 237-242. https://doi.org/10.5114/ain.2019.91004 Chicago Al-Imam, Ahmed, and Marek A Motyka. 2019. "On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence". Alcoholism and Drug Addiction/Alkoholizm i Narkomania 32 (3): 237-242. doi:10.5114/ain.2019.91004. Harvard Al-Imam, A., and Motyka, M. (2019). On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence. Alcoholism and Drug Addiction/Alkoholizm i Narkomania, 32(3), pp.237-242. https://doi.org/10.5114/ain.2019.91004 MLA Al-Imam, Ahmed et al. "On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence." Alcoholism and Drug Addiction/Alkoholizm i Narkomania, vol. 32, no. 3, 2019, pp. 237-242. doi:10.5114/ain.2019.91004. Vancouver Al-Imam A, Motyka M. On the necessity for paradigm shift in psychoactive substances research: the implementation of machine learning and artificial intelligence. Alcoholism and Drug Addiction/Alkoholizm i Narkomania. 2019;32(3):237-242. doi:10.5114/ain.2019.91004.
Cell Blocks: Evolution, Modernization, and Assimilation into Emerging Technologies

Cell blocks represent the convergence of cytology and histology. Processed as a formalin-fixed paraffin-embedded histology sample, the cell block is composed of cells and small tissue fragments condensed into a pellet. Unlike histology specimens, which have relatively consistent standard operating procedures, the protocols for cell blocks vary among laboratories. The reasons may be 2-fold. First, there is unavailability of an ideal cell block method – one that yields reproducible high cellularity, is not labor intensive and skill dependent, does not require the use of specific media, and is economical. Second, there is ongoing development and improvement of technologies in other aspects of medicine (e.g., minimally invasive procedures and molecular diagnostics) with demands to do more testing with less sample. As the traditional cell block methods have one or more shortcomings, there is ongoing interest in meeting the challenges and developing improved cell block methodologies to consistently provide high cellularity for diagnosis and ancillary testing. The basic techniques and attributes of traditional (e.g., plasma-thrombin, HistoGel) and newer methods, including the Cellient Automated Cell Block System, are discussed. The advantages of cell blocks, such as their ability to integrate with other existing technologies, such as whole-slide imaging, are also highlighted.
Human activity recognition based on extreme learning machine in smart home

Abstract This paper applies extreme learning machine (ELM)to human activity recognition in smart home, evaluates the human activity recognition model established by ELM. Experimental results show that the accuracy of activity recognition of ELM model is related to the number of hidden layer units. Too many/few hidden layer units can affect the performance of the ELM mode, apparently.
Developing the tasks-toward-transparency (T3) model for research transparency in open science using the lifecycle as a grounding framework

An increasingly data-intensive research environment has highlighted the need for greater research transparency to facilitate integrity and trust in open science and in the conduct of research more widely. The initial findings of faculty researchers' behaviors and practices toward research transparency, using the research data lifecycle as a grounding framework are reported. Four focus group sessions were conducted with faculty researchers in different disciplines, including natural sciences, social sciences, engineering, and different transparency practices were captured using the researchers' own language. Four generic transparency components were identified: action verb, object, task, and stage in the research lifecycle. The inter-relationships between specific transparency components were visualized using network visualizations. The network visualizations suggest that the lifecycle stages of Process/Visualize/Analyze and Publish/Preserve/Archive are key points for transparency, where 'data' play a critical role demonstrated by the multiple node-edge relationships. Based on the findings, a conceptual model, termed the Tasks-Toward-Transparency (T3) Model, was developed. This model may inform researcher practices and support research stakeholders whose role is to articulate and deliver transparency advocacy, policy, training programs, and services.
Inter AR: Interior Decor App Using Augmented Reality Technology

This paper focuses on the implementation of the mobile application that gives a virtual view of a reel furniture object in the real world by using Augmented Reality Technology. Augmented Reality (AR) is a technology that overlays the virtual objects in the environment of the real world. AR render’s the real-world information and present it in an interactive way so that virtual elements become part of the real world. This application will help the user to have a look and feel of the furniture object in the current environment before purchasing it from the store.