Reviewerly/exHarmony huggingface dataset:
Improved Lower Bounds on Second Order Non-linearities of Cubic Boolean Functions

Gode, Ruchi Telang Faruqi, Shahab Mishra, AshutoshNonlinearity of 2nd order is important in the study of Boolean functions from a cryptographic viewpoint. This property is useful in choosing efficient Boolean functions resisting quadratic approximation attacks. We obtain improved lower bounds with respect to the nonlinearities of 2nd order of some classes of degree 3 monomial Boolean functions of type $$g(x)=Tr( x^{2^i+2^j+1})$$ (here $$i,j\in \mathbb {N}$$ ) and $$i \le \lceil \frac{n}{2} \rceil $$ , $$ j \le i$$ . We were able to obtain these bounds in an efficacious way for Boolean functions upto 13 variables. Obtained results refine the bounds obtained by Gode and Gangopadhyay [10] and Mesnager et al. [21]. This shows the efficiency of these classes of functions with respect to nonlinearity of 2nd order.
A Fine-grained Optimization to Winograd Convolution Based on Micro-architectural Features of CPU

Deep Learning has become an infrastructure for a variety of AI applications. As a basic component of deep neural networks (DNNs), the convolution layer is compute-intensive and imposes a critical impact on the overall performance of deep learning. Accordingly, a large number of works pursue to accelerate the convolution layer by GPUs. We argue that the CPUs are able to supply comparable computing power with that of GPUs, and should be explored to expedite the deep learning as well. In this work, we focus on the widely-adopted Winograd convolution and propose a novel implementation based on the high performance CPUs equipped with AVX instructions.The Winograd convolution is well-known to achieve high performance by reducing the costly multiplication operations. However, our experiments demonstrate that the obtained performance is far from the theoretical analysis. In this paper, we analyze the state-of-the-art implementation of Winograd convolution on CPUs, investigating its three performance limitations including register block, data layout and cache block, and accordingly propose three measures, i.e., register block fixing, data layout adjustment, cache block searching, to improve the Winograd convolution in Intel DNNL library. The experiments show that our design is able to improve the original implementation by as many as 1.39&#x00D7;.
Comparing Personalized PageRank and Activation Spreading in Wikipedia Diagram-Based Search

Diagram Navigation (DN) is based on using existing diagrams for a domain as maps to navigate and query a collection from different perspectives. With a relatively small number of manual connections, such as ones between diagram concepts and related documents, a domain expert can integrate their perspective of a domain (depicted in a diagram) into the navigation system of a collection. DN utilizes the abundance of internal connections in a collection, such as Wikipedia hyperlinks to access the entire collection. In a Diagram-to-Content (D2C) query, an end user selects a diagram concept to retrieve a ranked list of related collection documents. In a Content-to-Diagram (C2D) query, DN highlights related concepts in a diagram based on document(s) selected by the user. To increase D2C ranking performance, we study and tune Personalized PageRank and an energy-spreading algorithm. We report key differences in how the algorithms rank D2C queries. We show that the tested algorithms are affected differently by Wikipedia graph structures, such as categories and hyperlinks from article templates. We also show that diagrams not only can provide overviews, but they also positively bias the ranking of D2C queries.
Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Model

At the end of December 2019, the COVID-19 virus was the initial report case in China Wuhan City. On March 11, 2020. The Department of Health (WHO) announced COVID-19, a global pandemic. The COVID-19 spread rapidly out all over the world within a few weeks. We will propose to develop a forecasting model of COV-19 positive case predict outbreak in Pakistan using Deep Learning (DL) models. We assessed the main features to forecast patterns and indicated The new COVID-19 disease pattern in Pakistan and other countries of the world. This research will use the deep learning model to measure several COVID-19 positive case reports in Pakistan. LSTM cell to process time-series data forecasts is very efficient. Recurrent neural network processes to handle time-dependent and involve hidden layers are confirmed and predict positive cases and weekly cases reported in the future. Bidirectional LSTM (Bi-LSTM) processes data and information in one direction to predict and analyze the weekly 6-9 days readily forecast the number of positive cases of COVID-19
Passive decoy-state quantum key distribution with imperfect source

Abstract Passive generation is a sophisticated way of state preparation in quantum key distribution (QKD) systems which is designed to exploit some internal physical process as a source of randomness. It can be profitable in a wide range of scenarios. However, the original analysis of the passive scheme implies an ideal interference which is almost impossible to assure in practice, therefore utilizing such method potentially compromises the security of the system. Here we develop a general technique to estimate decoy-state parameters for a passive protocol with an arbitrary experimental distribution of intensity. We compare this analysis with the original method and show that the proposed technique can provide higher key generation rates.
An Approximate Perspective on Word Prediction in Context: Ontological Semantics Meets BERT

This paper presents an analysis of a large neural network model—BERT, by placing its word prediction in context capability under the framework of Ontological Semantics. BERT has reportedly performed well in tasks that require semantic competence without any explicit semantic inductive bias. We posit that word prediction in context can be interpreted as the task of inferring the meaning of an unknown word. This practice has been employed by several papers following the Ontological Semantic Technology (OST) approach to Natural Language Understanding. Using this approach, we deconstruct BERT’s output for an example sentence and interpret it using OST’s fuzziness handling mechanisms, revealing the degree to which each output satisfies the sentence’s constraints.
A Pointer Instrument Reading Approach Based On Mask R-CNN Key Points Detection

In the field of automatic reading of instruments, the research direction is roughly divided into two categories. One is to study a kind of intelligent instrument which realizes automatic report reading from hardware. However, the cost of hardware optimization is too high. The other is to realize the reading function of the instrument by studying a suitable approach, so as to simplify the tedious steps of artificial operation. Algorithms already exist to implement pointer instrument readings, but these use either binarization or object detection. The binarization method is very sensitive to the background noise, while the object detection method still needs to binarize the image to detect the pointer after detecting the pointer instrument. So it is of great significance to find a robust and efficient approach to solve this problem. This paper proposes a pointer instrument reading approach based on Mask R-CNN to extract key points of the pointer instrument. The approach firstly uses Mask R-CNN to detect the prior bounding box of the pointer instrument, and then extracts key points of the pointer from the frame, the key points of the pointer are the center of the pointer instrument and the tip of the pointer. Compaired with other existing methods, our method is more precise and more flexible.
Decrease Iteration Time Deterministic Cyclic Scheduling for Real-time Periodic Tasks

Considering the characteristics of optimization theory and periodic tasks, this paper proposes a novel algorithm named as Decrease Iteration Time Deterministic Cyclic Scheduling (DITDCS). This is a soft real-time task allocation scheme based on optimization theory in a heterogeneous multi-core environment and uses task periodicity and DVFS (Dynamic Voltage and Frequency Scaling) to optimize the scheduling algorithm for computing time and system energy consumption. This algorithm creates a buffer to save a certain number of periodic tasks arriving and obtains the task allocation matrix in the buffer through the task allocation algorithm based on optimization theory. At the same time, after each task is assigned, the number of iterations in the calculation is reduced by updating the allocation matrix to improve the performance of the algorithm. Due to the characteristics of periodic tasks, the scheduling cycle with a certain length can be regarded as a scheduling template, which is used in subsequent scheduling to further reduce the computational overhead, and the effect is significant in a large-scale task set. Through experimental analysis, the proposed algorithm can achieve better energy consumption under the guarantee of task completion time and limited delay, and the overhead is also lower than the same type of algorithm.
A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator

The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. To find the global optimum, the SKF employs a Kalman filter mechanism that includes prediction, measurement, and estimate. However, the SKF is limited to operating in the numerical search space only. Numerous techniques and modifications have been made to numerical meta-heuristic algorithms in the literature in order to enable them to operate in a discrete search space. This paper presents modifications to measurement and estimation in SKF to accommodate the discrete search space. The modified algorithm is called Discrete Simulated Kalman Filter Optimizer (DSKFO). Additionally, the DSKFO algorithm incorporates the 2-opt operator to improve the solution in solving the travelling salesman problem (TSP). The DSKFO algorithm was compared against four other combinatorial SKF algorithms and outperformed them all.
Analysis of virtio GPU in a containerized environment

Virtio technology is a widely spread technology for emulating IO devices in virtualized environments. These environments are then called paravirtualized, because the guest system cooperates with the host system when performing IO tasks to achieve better performance. Virtio GPU is an emulated device for graphics, used for rendering and displaying. One of the benefits of Virtio devices is that most operating systems provide drivers and libraries for them. Despite the popularity of paravirtualization technology, its performance still falls short to those of containers. Containers offer near native speed because the guest and the host share the same kernel. This raises the question whether Virtio GPU device (and driver) should be utilized inside the container. This paper exposes the concept of how Virtio GPU can be integrated with a container, and what the main disadvantages of that solution are. An important lesson learned from this research is that Virtio GPU should not be used inside the container.
A Secure IWT Watermarking Technique using QR Decomposition and 2D Henon Maps

Watermarking techniques are employed for protecting copyrights, preventing digital thefts and providing proof-of-authentications, document integrity, tamper detection and localization. Therefore, strong imperceptible watermarking techniques provide an interesting research area of the present era. In this paper, we endeavour to improve capacity and imperceptibility of the watermark using Integer Wavelength Transform (IWT) with QR decomposition under a 2D Henon map. The host image is transformed using the Integer Wavelength Transform to produce a 2×2 block matrix, in which the top-left block is decomposed using the QR decomposition technique. The watermarking image is encrypted with a Henon map before embedding it into the decomposed part of the host image to enhance the security. The tests were conducted on sensitive Arabic text images and plausible results were achieved. It is shown that the proposed watermarking technique can be used for images in order to attain better tamper detection and localization.
Self-Driving Networks

This chapter presents a new vision of network operations, the self-driving network, that takes automation to the next level. This is not a description of existing work; rather, it is a challenge to dramatically rethink how we manage networks (or rather, how we do not manage networks). It draws upon an analogy with the development of self-driving cars and presents motivations for this effort. It then describes the technologies needed to implement this and an overall architecture of the system. As this endeavor will cause a major shift in network management, the chapter offers an evolutionary path to the end goal. Some of the consequences and human impacts of such a system are touched upon. The chapter concludes with some research topics and a final message. Key takeaways are that machine learning and feedback loops are fundamental to the solution; a key outcome is to build systems that are adaptive and predictive, for the benefit of users.
A 64 bit quantum dragon data-set for machine learning

Abstract Design and examples of a sixty-four bit quantum dragon data-set are presented. A quantum dragon is a tight-binding model for a strongly disordered nanodevice, but when connected to appropriate semi-infinite leads has complete electron transmission for a finite interval of energies. The labeled data-set contains records which are quantum dragons, which are not quantum dragons, and which are indeterminate. The quantum dragon data-set is designed to be difficult for trained humans and machines to label a nanodevice with regard to its quantum dragon property. The 64 bit record length allows the data-set to be utilized in restricted Boltzmann machines which fit well onto the D-Wave 2000Q quantum annealer architecture.
Character Mosaics: A New Form of Text-Based Artistic Representation

We propose character mosaics, a new form of text-based artistic representation. Unlike photographic mosaics that consist of hundreds of material images to form a larger motif image, our new approach places scaled characters to form the motif image. The tone of the motif is reproduced with the degree of magnification of hundreds of characters. This method keeps the context (the order of characters of an original text) so that viewers can read that text and at the same time see the motif such as author's portrait or landscape related to that text work. The proposed method would enhance viewers’ interest in various texts, their authors and background.
Aspect Based Opinion Mining on Hotel Reviews

Users often use online reviews to assess the quality of hotels according to their various attributes. In this study, a sentiment analysis of online reviews has been conducted using 11 predetermined attributes pertaining to hotels. Using this analysis, users’ overall assessments of hotels have been determined and summarized from reviews left for a group of various hotels. To identify words with similar meanings to the 11 predetermined hotel attributes, the Word2Vec method has been employed. Additionally, the FastText method has been used to detect words containing spelling errors. The sentiment analysis of the comments has been made by using three different methods belonging to two different approaches. These methods are: VADER method as dictionary-based approach, BERT and RoBERTa as machine learning approaches. Using these methods, the reviews have been evaluated in three categories as positive, negative, and neutral, and the quality score has been calculated. In addition, a software with a user-friendly graphical interface has been implemented in an effort to easily use all the methods used in this study.
Prototype Blockchain Based Smart Contract For Freelance Marketplace System

Freelancing marketplace is a site or platform that connects two parties in processing service transactions at an hourly rate or per project. A conventional freelancing marketplace is a place for freelancers to find work and transact digitally. This study aims to propose a prototype of a freelancing marketplace system that is distributed and decentralized, secure, and transparent using smart contract-based blockchain technology. The method used in this study is a prototype which is a fast method of developing a software system. The developed prototype is a system that is based on the Ethereum public blockchain network, utilizes a smart contract mechanism in its transaction activities, and use IPFS in the storage and sharing of documents on it. According to the findings of the research, transaction data input in the freelancing marketplace system prototype environment can be executed by smart contracts and saved on the blockchain network, indicating that the transaction data will be stored more securely, tamper proof, and transparent.
Comics for inclusive English language learning: the CIELL app, supporting dyslexic English language learners

As teaching moves increasingly online, language teachers are faced with the challenge of how to support dyslexic students in an inclusive manner in and out of the classroom. This paper will focus on an innovative educational multi-modal, mobile application – Comics for Inclusive English Language Learning (CIELL) – supporting upper-intermediate and advanced English as a Foreign Language (EFL) students with dyslexia when faced with language proficiency tests and academic writing tasks. A cyclical educational design research methodology (McKenney &amp; Reeves, 2019) was used to include three cycles of feedback from stakeholders so that their views and suggestions would inform the development of an alpha, beta, and gamma version of the app, thereby maximising practical relevance. The discussion of the quantitative and qualitative feedback is supported by educational design research.
Employee Development Program: A Generation Model for Project-based Learning Implementation at the University

This paper represents a methodological approach for university staff development programs; it should be considered at this juncture as a work-in-progress paper. The considered methodological approach's main feature is to create a personalized level assessment of the university staff competencies. This is accomplished through an analysis of the university's digital services capabilities, with the objective of facilitating the further development and implementation of project-based learning. Based on the evaluation, a unique prescribed educational path is formed for each employee, depending on his/her role in the project-based learning. This path is created considering not only the necessary competency levels of the individual, but also the participant's role in the project-based learning implementation. The methodological approach was tested as part of the project-based learning implementation at the Ural Federal University. Studies were conducted on a sample of 108 people tested. The proposed mechanism holds an undeniable advantage in its scalability and ease of implementation when making minor changes and updating content for a specific task. It is worth mentioning that it can be used at any level educational institution or in a private sector enterprise that is implementing employee training.
Asking the Right Questions: Facilitating Semantic Constraint Specification for Robot Skill Learning and Repair

Developments in human-robot teaming have given rise to significant interest in training methods that enable collaborative agents to safely and successfully execute tasks alongside human teammates. While effective, many existing methods are brittle to changes in the environment and do not account for the preferences of human collaborators. This ineffectiveness is typically due to the complexity of deployment environments and the unique personal preferences of human teammates. These complications lead to behavior that can cause task failure or user discomfort. In this work, we introduce Plan Augmentation and Repair through SEmantic Constraints (PARSEC): a novel algorithm that utilizes a semantic hierarchy to enable novice users to quickly and effectively select constraints using natural language that correct faulty behavior or adapt skills to their preferences. We show through a case study that our algorithm efficiently finds corrective constraints that match the user’s intent, providing a path for novice users to exploit the advantages of constrained motion planning combined with human-in-the-loop skill training.
Game environment art with modular architecture

3D games routinely turn to modular architecture as an optimization technique for environment art and level design, although there is little documentation of how to implement it. As a result, many games struggle to apply it effectively, contributing to problems such as art fatigue and production delays. This investigation proposes a set of unified principles of modular architecture for 3D game environment art based on traditional architecture and game examples. It finds that the use of a uniform grid and standard measurements are key as well as extensive planning, and that assets organized as modular kits can be more cost-effective and facilitate flexible level design. It concludes with a series of steps per development phase of a game’s lifecycle aimed at game environment art, with emphasis on early planning and testing, and suggests that close collaboration between environment artists and level designers is key to develop effective modular assets.
Semantic Social Event Evolution Discovering

People tend to express their opinions and emotions on social platforms, such as Weibo and Twitter. Although recent researches can discover topics and detect events from social messages, people pay more attention to the evolution analysis of events. However, existing methods only utilize the statistical characteristics of the text instead of semantic information. In this paper, we construct a 6-tuple to represent events to analyze the correlation between events from multiple perspectives. In order to mine the evolutionary relationship between events, we develop the method for measuring event similarity by concerning entities and texts. In terms of textual similarity measuring, we do a domain-adaptive incremental training task on the pre-trained NLP model to gain word embeddings for semantic information. Experiments on real social datasets show that our technique precedes the baseline technique.
Information Retrieval Using n-grams

Information retrieval is a self-explanatory term in today’s era and so is information overload. Retrieving the right information from a plethora of documents can be an overwhelming but a much-needed task. Existing techniques include building and querying Boolean incidence matrix, inverted term and positional index, vector space model, support vector machines and other machine learning-based approaches. n-grams is a contiguous occurrence of n terms in a text. Searching n-grams or terms in close proximity to each other in a set of documents has a lot of applications in today’s data intensive age like plagiarism detection, text classification, etc. This work proposes an algorithm and its optimized variants to find the occurrence of n-gram in the corpus of documents indexed using a positional index. The optimized variant is aimed to reduce the number of search operations.