Reviewerly/exHarmony huggingface dataset:
The Evolution of Garbage Collection in V8: Google's JavaScript Engine

Runtime environments allow for dynamic memory management, without explicit allocations and deallocations from programmers. Thus, algorithms that are responsible for identifying live objects and freeing the unused memory are triggered throughout the application execution. Garbage collection algorithms can cause delays and affect the overall performance. Google's V8 JavaScript engine is a runtime environment that translates JavaScript to machine code and implements garbage collection. The engine is used, as an autonomous system or module, in projects such as: the Chrome web browser and Node.js with multi-platform support. Thus, there is a wide range of applications including real-time systems and web applications on the cloud, desktop, server and mobile devices and with different micro processor architecture (ARM, MIPS etc.) that use directly or indirectly the engine. This paper surveys the V8 virtual machine infrastructure on the techniques for managing memory dynamically. The focus is on memory and object layout, as well as the garbage collection techniques used. Furthermore, the paper defines the specific constraints that are imposed on V8, outlines historical approaches of solving the problem and describes its current state. Finally, it presents research directions that can be explored in garbage collection using the V8 runtime.
Mobile Based Agricultural Management System for Indian Farmers

Agriculture is one of the most powerful tools to eradicate hunger and poverty from the world. Technology has been playing an essential role in the agricultural sector of developed economies, but when it comes to developing and under-developed nations, there is a need to bridge the gap between technology and agriculture. The efficiency in the crop productivity and optimum utilization of resources is a challenge that can be addressed with technological advancement. This study aims to create a knowledge-based system (KBS) in the form of a mobile application to help the Indian farmers for improvement in their agricultural practices and increasing crop productivity. A prototype mobile application, 'Farm-n-pedia,' is used to fulfill the informational needs of the farmers and provide a tool for agriculture management using a single platform. This agricultural knowledge mobile application provides accessibility and personalization, which is long desired by the farmers. It enables the users to access any information they want from all around the globe, get personalized expert guidance, know about the latest farming techniques and technology, and increase agricultural productivity.
Prediction of Direct Normal Irradiation Based on CNN-LSTM Model

Due to the intermittency and instability of power output of solar power stations (SPS), the safety and stationarity of the electric grid are facing considerable challenges. So, it is essential to predict the power output of SPS accurately. To date, as the most important meteorological factor determining the power output of SPS, the prediction of direct normal irradiation (DNI) has always been a hotspot, however at the same time a challenging problem. In recent years, with the improvement of hardware performance, deep learning methods have witnessed rapid development in the field of forecasting of renewable energies. In this paper, a hybrid method combining convolutional neural network (CNN) and longshort term memory (LSTM) neural network is proposed, the former can be effectively used for extraction of spatial feature, while the latter is used for extraction of temporal feature. As a case study, the proposed method is used for prediction of DNI of four typical days, i.e. spring equinox, summer solstice, autumn equinox, and winter solstice, based on the meteorological conditions of Zhangbei. The results show that deep learning methods can be effectively used for prediction of DNI, however, the proposed method in this paper achieves higher prediction accuracy than that of the compared methods.
InterCriteria Analysis of Public Health Data in Bulgaria

The assessment of the system of public health has been receiving significant importance over the past years, because it reflects the trend in optimizing the effectiveness of the health services. The article uses a recently defined intelligent decision-making method based on an extension of fuzzy logic called InterCriteria Analysis (ICA). Object of the study of (the) ICA are real data in the field of public health connected with in-patient, out-patient health care establishments and their regional distribution in Bulgaria for the years 2010–2018. Using the ICA approach, we can identify the relationships between and among indicators of health care facilities and nursing staff, statistical regions and districts, the doctors in the healthcare facility by medical specialties, and more. The metrics that have the greatest dependencies and the contrary indicators that are often independent of one another. This way we can monitor their demeanor over time.
From Leonardo to the Next Rembrandt – The Need for AI-Pessimism in the Age of Algorithms

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The development of synthetic compounds in German: Relating diachrony with L1 acquisition

The development of synthetic compounds with deverbal heads in German, namely nominalizations with ung (such as Kindererziehung ‘child education’) and the nominalized infinitive (such as Eierlegen ‘laying of eggs’) has not been studied for language acquisition, due to their late emergence and the poor documentation in later acquisition stages. The historical emergence of synthetic compounding has had little attention. Our aim is to bring together both ‘emergence-driven’ perspectives for investigating what formal properties of synthetic compounding can be observed from the perspective of the most frequent nominalization patterns of present-day German for abstract nouns. The theoretical comparison shows that both developments (child language development and the historical development) display an increase in morphological complexity: while both kinds of nominalizations start with simple verbs, prefix and particle verbs follow. In a next step, the nominalization patterns are widened in favor of complex bases.
Designing an Innovative Collaborative Learning Application: The Case of Method 300

The number of mobile learning applications is increasing and they are considered a powerful learning tool by many researchers. Among the reasons for this success are their specific characteristics, such as portability and pervasiveness, which can help student engagement and improve learning. They can also be useful for collaborative learning. This paper presents the methodology that is being used to design the Method 300 application. Method 300 is based on an active and collaborative approach that aims to improve the learning process of a team of students. At the moment, it is applied with the help of some tools, such as forms and spreadsheets. While these tools are useful, they make it somewhat laborious for teachers to organize all the documents, especially for large classes. This paper describes the empathic and collaborative approach to designing the Method 300 app, while placing educational, design and computational issues on the same level of relevance to incorporate the decisions made. The focus of this paper is to describe the application development methodology and not the application itself. Initially, a review about collaborative learning applications is presented. Then, Method 300 is presented, followed by the description of the methodology for designing the application. It is concluded that the Method 300 app has great potential to spread this method worldwide and that the empathic multidisciplinary collaborative approach to designing this application can be considered in different contexts.
Teaching Secure Acquisition in Higher Education

Acquisition is the most common method for introducing new technology into organizations, but security and integrity are difficult to guarantee using conventional acquisition techniques. Best practices for secure acquisition are an essential part of the general education process in cybersecurity.
Effect of Motion Cues on Simulator Sickness in a Flight Simulator

The objective of this study is to investigate the effect of sensory conflict on the occurrence and severity of simulator sickness in a flight simulator. According to the sensory conflict theory, it is expected that providing motion cues that match the visual cues will reduce the discrepancy between the sensory inputs and thus reduce simulator sickness. We tested the effect of motion cues thorough a human subject experiment with a spherical type motion platform. After completing pre-experiment questionnaire including Motion Sickness Susceptibility Questionnaire (MSSQ) and Immersive Tendency Questionnaire (ITQ), two groups of participants conducted a flight simulation session with or without motion cues for 40 min. In the simulation session, participants were asked to fly through the gates sequentially arranged along the figure-eight shaped route. The Simulator Sickness Questionnaire (SSQ) was filled out after the exposure to compare groups between with and without motion cues. Physiological data, including electrodermal activity, heart rate, blood volume pressure, and wrist temperature were also collected to find the relationship with perceived simulator sickness. The results showed that simulator sickness and disorientation significantly lowered in motion-based group. Also, nausea and oculomotor were marginally lower when motion cue was given. This study supports sensory conflict theory. Providing proper motion cue corresponding to the visual flow could be considered to prevent simulator sickness.
Character sums over affine spaces and applications

Given a finite field Fq, a positive integer n and an Fq-affine space A⊆Fqn, we show that a result of Katz provides a new bound on the sum ∑a∈Aχ(a), where χ is a multiplicative character of Fqn. The paper is focused on the applicability of this estimate to results regarding the existence of special primitive elements in Fqn. In particular, we obtain substantial improvements on previous works.
Generation of Call Graph for Java Higher Order Functions

The Lambda expression introduced in Java 8 gives a functional style to the object-oriented program. The major highlights of lambda expression include lazy evaluation, code readability, avoiding code duplication. A static call graph can be used to visualize every possible run that the program might take. Due to the recent software development using Java, the new features aintroduced in Java 8 may be explored. Hence this demands the need for the call graphs generated for such software. This paper suggests an algorithm for the construction of a call graph for the lambda constructs. A static call graph will be generated by preserving the signature of the methods. The model suggested here uses an intermediate Abstract syntax tree (AST) like representation which is further transformed and optimized into a call graph representation.
Secure Multiparty Learning from Aggregation of Locally Trained Models

In this paper, we propose a new protocol for secure multiparty learning (SML) from the aggregation of locally trained models, by using homomorphic proxy re-encryption and aggregate signature techniques. In our scheme, we utilize the method of secure verifiable computation delegation to privately generate labels for auxiliary unlabeled public data. Based on the labeled dataset, a central entity can learn a global learning model without direct access to the local private datasets. The generalization performance of SML is excellent and almost equals to the accuracy of the model learned from the union of all the parties’ datasets. We implement SML on MNIST, and extensive analysis shows that our method is effective, efficient and secure.
Perceptual Linear Prediction Feature as an Indicator of Dysphonia

Voice is the most widely used form of communication in humans. The analysis of human voice is one of the areas that has been of great significance. This area is studied for its various applications in the field of medicine and engineering. The analysis in general deals with the extraction of some parameters of the voice signal for investigation and processing. A system that diagnoses the patients from their speech signals is proposed in this work. It includes detection and classification of certain common medical conditions which affect the voice patterns of the patients. The feature vectors of the speech samples are obtained by using perceptual linear prediction (PLP) and relative spectral transform perceptual linear prediction (RASTA–PLP) feature extraction methods. The detection and classification of voice pathology are done by using the support vector machine (SVM) classifier. The accuracy of the classifier is computed using speech samples of gastroesophageal reflux disease (GERD) and vocal fold paralysis from the pre-classified database.
Communication vs Synchronisation in Parallel String Comparison

The longest common subsequence (LCS) problem is fundamental in computer science and its many applications. Parallel algorithms for this problems have been studied previously, in particular in the bulk-synchronous parallelism (BSP) model, which treats local computation, communication and synchronisation as independent scarce resources of a computing system. We consider primarily BSP algorithms running in either constant or polylogarithmic synchronisation. Based on our previous results on the algebraic structure and efficient algorithms for semi-local LCS, we present BSP algorithms for parallel semi-local LCS, improving on the existing upper bounds on communication and synchronisation; in particular we present the first constant-sync work-optimal LCS algorithm.
An Adaptive Instructional System for the Retention of Complex Skills

Many professional operations require employees with complex skills. Once these skills have been taught, it is important that the skills are (a) retained, and (b) retrained when the skills start to decay. The ability to determine the precise moment when a skill needs to be retrained will have a positive effect on the productivity of the individual, and therefore also on the cost-effectiveness of scheduled training courses. However, modelling the retention of complex skills remains challenging as it is difficult to gather enough data. In this paper, we present an online adaptive instructional system that serves two purposes: (1) to gather performance data on a complex video game called Space Fortress, so that the skill retention can be modelled, and (2) to apply the newly built model directly to the participants, so that its effectiveness can be analysed. We expect that the lessons learned by building and applying the model in the context of Space Fortress will transfer to complex real-world skills.
Password Strength Analyzer Using Segmentation Algorithms

Passwords are as yet the prevailing path for user verification and system security while other validation techniques exist. So selecting a strong password is a challenging thing. Passwords are used in every walk of life like logging into a system, creating an account in an online store, banking, etc. People struggle to remember all of their passwords and so they prefer to use passwords based on their personal attributes viz.their name, spouse name, date of birth, etc. Choosing such passwords could be at the risk of being hacked. Generally the strength of passwords is checked based on the general guidelines like it should contain alphanumeric characters, special symbols, etc. But it never checks whether the chosen password is based on user attributes. Our proposed system checks the strength of passwords through segmentation algorithms and analyses whether the chosen password is based on user attributes and it is considered to be a weak password and strong if it is not based on user attributes. In this paper, an optimal segmentation algorithm named Password Segmentation Algorithm is proposed to segment password and user attributes, and later the correlation between a segmented password and user attributes is done. Passwords with less correlation of private details are the safest to use.
Euler Well-Composedness

In this paper, we define a new flavour of well-composedness, called Euler well-composedness, in the general setting of regular cell complexes: A regular cell complex is Euler well-composed if the Euler characteristic of the link of each boundary vertex is 1. A cell decomposition of a picture I is a pair of regular cell complexes $$\big (K(I),K(\bar{I})\big )$$ such that K(I) (resp. $$K(\bar{I})$$ ) is a topological and geometrical model representing I (resp. its complementary, $$\bar{I}$$ ). Then, a cell decomposition of a picture I is self-dual Euler well-composed if both K(I) and $$K(\bar{I})$$ are Euler well-composed. We prove in this paper that, first, self-dual Euler well-composedness is equivalent to digital well-composedness in dimension 2 and 3, and second, in dimension 4, self-dual Euler well-composedness implies digital well-composedness, though the converse is not true.
Deep learning algorithm for Gaussian noise removal from images

A deep learning algorithm for Gaussian noise removal from both grayscale and color images is developed. As opposed to most existing discriminative methods that train a specific model for each noise level, the proposed method can handle a wide range of noise levels using only two trained models, one for low noise levels and the other for high noise levels. In the proposed algorithm, the training process consists of three successive steps. In the first step, a classifier is trained to classify the noisy and clean images. In the second step, a denoiser network aims to remove the noise in the image features that are extracted by the trained classifier. Finally, a decoder is utilized to map back the denoised images features into images pixels. To evaluate the performance of the model, the Berkeley segmentation dataset of 68 images (BSDS68) and 12 widely used images are used, and the denoising performance for additive white Gaussian noise is compared with several state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and visual quality. For grayscale image denoising of BSDS68, our method gives the highest PSNR on all noise levels (significant mean improvement of 0.99). For color image denoising of BSDS68, except for one low noise level, the proposed method gives the highest PSNR on all other noise levels (mean improvement of 0.3).
Communication Lower Bounds of Convolutions in CNNs

Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs). Due to the complex data dependency and the increase in the amount of model samples, the convolution suffers from high overhead on data movement. This work provides comprehensive analysis and methodologies to minimize the communication for the convolutions in CNNs. With an in-depth analysis on the I/O complexity theory under the red-blue pebble game model, we develop a general communication lower bound theory for a composite algorithm which consists of several different sub-computations. Based on the proposed theory, we establish the data movement lower bound results for three main convolution algorithms in CNNs, which are the direct convolution, the image2col method and Winograd algorithm. Furthermore, derived from I/O lower bound results, we design the near communication-optimal strategies respectively for the three main convolution algorithms by fully exploiting the data reuse. The deep analysis demonstrates that our designs are able to nearly reach the minimum communication in a two-level memory hierarchy.
Hierarchical Improvement of Quantum Approximate Optimization Algorithm for Object Detection: (Invited Paper)

Quantum Approximate Optimization Algorithm (QAOA) provides approximate solution to combinatorial optimization problems. It encodes the cost function using a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -level quantum circuit where each level consists a problem Hamiltonian followed by a mixing Hamiltonian. Despite the promises, few real-world applications (besides the pedagogical MaxCut problem) have exploited QAOA. The success of QAOA relies on the classical optimizer, variational parameter setting, and quantum circuit design and compilation. In this study, we implement QAOA and analyze its performance for a broader Quadratic Unconstrained Binary Optimization (QUBO) formulation to solve real-word applications such as, partially occluded object detection problem. Furthermore, we analyze the effects of above influential factors on QAOA performance. We propose a 3-level improvement of hybrid quantum-classical optimization for object detection. We achieve more than 13X execution speedup by choosing L-BFGS-B as classical optimizer at the first level and 5.50X additional speedup by exploiting parameter symmetry and more than 1.23X acceleration using parameter regression at the second level. We empirically show that the circuit will achieve better fidelity by optimally rescheduling gate operations (especially for deeper circuits) at the third level.
Improving Authentication and Data Sharing Capabilities of Cloud using a Fusion of Kerberos and TTL-based Group Sharing

Cloud security has been of utmost concern for researchers and cloud deployers since the inception of cloud computing. Methods like PKI, hashing, encryption, etc. have proven themselves useful throughout cloud technology development, but they are not considered as a complete security solution for all kinds of cloud authentications. Moreover, data sharing in the cloud has also become a question of research due to the abundant use of data storage available on the cloud. To solve these issues, a Kerberos-based time-to-live (TTL) inspired data sharing and authentication mechanism is proposed on the cloud. The algorithm combines the two algorithms and provides a better cloud deployment infrastructure. It uses state-of-the-art elliptic curve cryptography along with a secure hashing algorithm (SHA 256) for authentication, and group-based time-to-live data sharing to evaluate the file-sharing status for the users. The result evaluates the system under different authentication attacks, and it is observed that the system is efficient under any kind of attack and any kind of file sharing process.
Wizardry in Distributed Participatory Design

Many participatory design methods and techniques assume that the designer and the participant are in the same location. In this study, we explore methods for Distributed Participatory Design. Combining the Wizard-of-Oz technique with oral discussions, in three iterations, we allowed users to actively participate, over distance, in designing a solution for their Events Management and Booking System. A video prototype was captured and used as a specification to communicate the system requirements. System development issues that arose were captured in a log and used to explore the effectiveness of such a specification in the development process. The results show that using wizardry in distributed participatory design is a viable method for allowing active participation and that video prototypes can sufficiently communicate system requirements.