Reviewerly/exHarmony huggingface dataset:
Relaxed Locally Correctable Codes in Computationally Bounded Channels

Error-correcting codes that admit local decoding and correcting algorithms have been the focus of much recent research due to their numerous applications. An important goal is to obtain the best possible tradeoffs between the number of symbols of the codeword that the local decoding algorithm must examine (the locality), and the amount of redundancy in the encoding (the information rate). In Hamming's classical adversarial channel model, the current tradeoffs are dramatic, allowing either small locality but superpolynomial blocklength, or small blocklength but high locality. However, in the computationally bounded adversarial channel model, proposed by Lipton (STACS 1994), constructions of locally decodable codes suddenly exhibit small locality and small blocklength, but these constructions require strong trusted setup assumptions. We study variants of locally decodable and locally correctable codes in computationally bounded, adversarial channels, in a setting with no trusted setup. The only assumption we require is the selection of the public parameters (seed) for a collision-resistant hash function. Specifically, we provide constructions of relaxed locally correctable and relaxed locally decodable codes over the binary alphabet, with constant information rate, and poly-logarithmic locality. Our constructions, which compare favorably with their classical analogs, crucially employ collision-resistant hash functions and local expander graphs, extending ideas from recent cryptographic constructions of memory-hard functions.
Preserve Integrity in Realtime Event Summarization

Online text streams such as Twitter are the major information source for users when they are looking for ongoing events. Realtime event summarization aims to generate and update coherent and concise summaries to describe the state of a given event. Due to the enormous volume of continuously coming texts, realtime event summarization has become the de facto tool to facilitate information acquisition. However, there exists a challenging yet unexplored issue in current text summarization techniques: how to preserve the integrity, i.e., the accuracy and consistency of summaries during the update process. The issue is critical since online text stream is dynamic and conflicting information could spread during the event period. For example, conflicting numbers of death and injuries might be reported after an earthquake. Such misleading information should not appear in the earthquake summary at any timestamp. In this article, we present a novel realtime event summarization framework called IAEA (i.e., Integrity-Aware Extractive-Abstractive realtime event summarization). Our key idea is to integrate an inconsistency detection module into a unified extractive–abstractive framework. In each update, important new tweets are first extracted in an extractive module, and the extraction is refined by explicitly detecting inconsistency between new tweets and previous summaries. The extractive module is able to capture the sentence-level attention which is later used by an abstractive module to obtain the word-level attention. Finally, the word-level attention is leveraged to rephrase words. We conduct comprehensive experiments on real-world datasets. To reduce efforts required for building sufficient training data, we also provide automatic labeling steps of which the effectiveness has been empirically verified. Through experiments, we demonstrate that IAEA can generate better summaries with consistent information than state-of-the-art approaches.
ICSA-ECNN based Image Forgery Detection in Face Images

Image forgery detection places a vital role in today's world. Recently, Major research work is being in process in this domain. This paper proposed a novel approach for enhanced convolution neural network-based Forgery detection techniques employed. Preprocessing is done at the initial stage to convert RGB to LAB space conversion, Proceeded with feature selection by employing image integration. The next step is to extract the features like kurtosis, Maximum Probability, Block artificial Grid. In Continuation with feature extraction, feature selection is performed using an improved crow search algorithm followed by enhanced convolution neural network classification. Finally, the performance of the proposed improved crow search algorithm is discussed.
Working Together in a PhamilySpace

Studies have shown that interpersonal relationships such as families and friends are an important source of support and encouragement to those who seek to engage in healthier habits. However, challenges related to geographic distance may hinder those relationships from fully collaborating and engaging in healthy living together. To explore this domain, we developed and deployed a lightweight photo-based application called PhamilySpace with a week-long intervention. Our goal is to examine family members' and friends' engagement and awareness on healthy behaviors while living apart. Our analysis of the semi-structured interviews, pre/post-intervention instruments, and application logs suggests three main benefits of interventions for health promotion in this context: (1) increased awareness on acts of health; (2) reciprocal sharing of health information supports social accountability over distance; and (3) positive dialogue around health enhances support on healthy living. By providing insights into distributed family/friends interactions and experiences with the application, we identify benefits, challenges, and opportunities for future design interventions that promote healthy behaviors.
An FPGA-Based Neural Network Overlay for ADAS Supporting Multi-Model and Multi-Mode

Advanced Driver-Assistance Systems (ADAS) are complex systems consisting of many computer vision tasks including image classification, object detection and semantic segmentation. FPGA is a feasible solution for deep learning based computer vision accelerator due to its high performance and energy efficiency. However, design a high performance FPGA accelerator requires good understanding of basic hardware concepts and consumes a long compilation time. Overlays can alleviate the above problems by accelerating applications in a software via a hardware architecture and a compiler. In this paper, we propose an FPGA-based neural network overlay processor for ADAS. The overlay architecture contains almost all common computation layers for learning based ADAS. In addition, we design a compiler that can automatically compile the high-level description of neural networks from deep learning framework like Caffe and Tensorflow into FPGA configurable codes, which can be executed by our overlay architecture without reprogramming. Experiments show that our overlay can process learning tasks in ADAS with low latency and low memory usage.
An Abstract View on Optimizations in SAT and ASP

Search-optimization problems are plentiful in scientific and engineering domains. MaxSAT and answer set programming with weak constraints (ASP-WC) are popular frameworks for modeling and solving search problems with optimization criteria. There is a solid understanding on how SAT relates to ASP. Yet, the question on how MaxSAT relates to ASP-WC is not trivial. The answer to this question provides us with the means for cross fertilization between distinct subareas of automated reasoning. In this paper, we propose a weighted abstract modular framework that allows us to (i) capture MaxSAT and ASP-WC and (ii) state the exact link between these distinct paradigms. These findings translate, for instance, into the immediate possibility of utilizing MaxSAT solvers for finding solutions to ASP-WC programs.
Spectrum decomposition in Gaussian scale space for uneven illumination image binarization

Although most images in industrial applications have fewer targets and simple image backgrounds, binarization is still a challenging task, and the corresponding results are usually unsatisfactory because of uneven illumination interference. In order to efficiently threshold images with nonuniform illumination, this paper proposes an efficient global binarization algorithm that estimates the inhomogeneous background surface of the original image constructed from the first k leading principal components in the Gaussian scale space (GSS). Then, we use the difference operator to extract the distinct foreground of the original image in which the interference of uneven illumination is effectively eliminated. Finally, the image can be effortlessly binarized by an existing global thresholding algorithm such as the Otsu method. In order to qualitatively and quantitatively verify the segmentation performance of the presented scheme, experiments were performed on a dataset collected from a nonuniform illumination environment. Compared with classical binarization methods, in some metrics, the experimental results demonstrate the effectiveness of the introduced algorithm in providing promising binarization outcomes and low computational costs.
Toward society 5.0: Indonesia and Japan on the 21<sup>st</sup> century literacy skills

Abstract Ever since its declaration in 2016 by the Japanese government, the society 5.0 concept has been largely adopted by global communities. It is argued that to implement the concept successfully, the research and the teaching of foundational skills like numeracy, literacy, or ICT at the elementary school level should be mandatory. This article reports the research and the teaching of the 21 st literacy skills of Japan and Indonesia in the past four years (2016 - 2020). We reviewed the research and the teaching of 21 st century skills at the elementary education level in both countries. The research databases used to search the literature were Google Scholar, ERIC, Education Abstracts, PsycINFO, and Social Sciences Citation Index. The inclusion criteria used to sort the studies were literacy skills/elementary/Japan/Indonesia/digital literacy/information literacy/ITS literacy and limited to studies that were done from 2016 – 2020 After reviewing ten studies, this pilot systematic review study preliminary found that open-access studies on the 21 st literacy skills in Japan’s elementary school were restricted, so it was harder to find than studies that discussed similar topics in the Indonesian context. Existing studies in Japan at the elementary level prioritize the research and the teaching of literacy skills of information, ICT, and foreign language literacies. In contrast, studies in Indonesia focus on basic, scientific, and foreign language literacies.
Pathology reporting: communication is key

<h2>Abstract</h2> Guidelines generally focus on complete reporting of histopathology data in a standard format. We highlight the importance of effectively communicating the "message" to clinicians who do not view histology images. Communication issues related to each part of the histopathology report are outlined. We emphasize that reports must be unambiguous and include only relevant information so that the latter is not misunderstood or missed by the treating clinician. The specimen block key should be available to reporting and reviewing pathologists but does not have to be included in the report communicated to the clinical team. Pathologists should also be aware that they may be communicating directly with patients who read histopathology reports. Grade and stage represent biological continuums. In truly borderline cases, effective communication may be more important than precise grading or staging. We also provide advice on recording of other opinions that had been sought while reporting a case.
Neural Network Based Deep Transfer Learning for Cross-Domain Dependency Parsing

In recent years, dependency parsers perform well on the in-domain data, but perform poor on the out-of-domain. In this paper, we present a deep neural network for cross-domain dependency parsing. Our system is based on the stack-pointer networks (STACKPTR). Considering the importance of context, we utilize self-attention mechanism for the representation vectors to capture the meaning of words. In addition, to adapt three different domains, we utilize neural network based deep transfer learning which transfers the pre-trained partial network in the source domain to be a part of deep neural network in the three target domains (product comments, product blogs and web fiction) respectively. Results on the three target domains demonstrate that our model performs competitively.
Linear Dilation-Erosion Perceptron Trained Using a Convex-Concave Procedure

Mathematical morphology (MM) is a theory of non-linear operators used for the processing and analysis of images. Morphological neural networks (MNNs) are neural networks whose neurons compute morphological operators. Dilations and erosions are the elementary operators of MM. From an algebraic point of view, a dilation and an erosion are operators that commute respectively with the supremum and infimum operations. In this paper, we present the linear dilation-erosion perceptron (\(\ell \)-DEP), which is given by applying linear transformations before computing a dilation and an erosion. The decision function of the \(\ell \)-DEP model is defined by adding a dilation and an erosion. Furthermore, training an \(\ell \)-DEP can be formulated as a convex-concave optimization problem. We compare the performance of the \(\ell \)-DEP model with other machine learning techniques using several classification problems. The computational experiments support the potential application of the proposed \(\ell \)-DEP model for binary classification tasks.
Analysing Keyword Lists

Frequency lists are useful in their own right for assisting a linguist, lexicographer, language teacher, or learner analyse or exploit a corpus. When employed comparatively through the keywords approach, significant changes in the relative ordering of words can flag points of interest. This conceptually simple approach of comparing one frequency list against another has been very widely exploited in corpus linguistics to help answer a vast number of research questions. In this chapter, we describe the method step-by-step to produce a keywords list, and then highlight two representative studies to illustrate the usefulness of the method. In our critical assessment of the keywords method, we highlight issues related to corpus design and comparability, the application of statistics, and clusters and n-grams to improve the method. We also describe important software tools and other resources, as well as providing further reading.
Cryptanalysis of Vigenère Cipher: Method Implementation

Cryptanalysis is the technique of analysing and breaking an encrypted text (ciphertext) to obtain the clear text (plaintext). Cryptanalysis analyses all the information available about the examined methods. This information may be public, originate from plausible assumptions or be able to specifically learn. The type of information available is divided into different attack scenarios to qualify the relevance of the attack or comment on security. In this paper, we will provide an implementation of cryptanalysis methods by cryptanalysing ciphertexts by using Vigenère cipher. The paper shows how the algorithms remain simple enough to implement the message without errors. This paper will allow researchers to better understand the technique's use of mathematical formulas to analyse secure information systems to detect vulnerabilities and shrouded components.
Digital Literacy: A Catalyst for the 21<sup>st</sup>Century Education

The seamless integration of new digital technologies into higher education teaching and learning has transformed education pedagogies and changed how students learn. The students are now required to have digital competencies to survive in the era of learning with technology; therefore, measuring the students' digital competencies is of utmost importance. This study evaluates the first-year university students' digital competencies at a higher education institute using a newly designed digital literacy measuring tool named digitlitfj. The digitlitfj is an online tool consisting of a 5 point Likert scale questionnaire ranging from `No understanding' to `Advanced level of understanding' that was piloted to the first-year university students. The results show that 86.15% of the students were average to very highly digitally literate. Also, Deep learning, Support Vector Machine, Random Forest and Decision Tree algorithms in RapidMiner were used to evaluate the most important and influential variables in predicting an individual's digital literacy competency. The results show that all the variables utilized in the research were important, with computer literacy being the most influential variable in predicting an individual's digital literacy.
The Norm Convergence of a Least Squares Approximation Method for Random Maps

We prove the [Formula: see text]-norm and bounded variation norm convergence of a piecewise linear least squares method for the computation of an invariant density of the Foias operator associated with a random map with position dependent probabilities. Then we estimate the convergence rate of this least squares method in the [Formula: see text]-norm and the bounded variation norm, respectively. The numerical results, which demonstrate a higher order accuracy than the linear spline Markov method, support the theoretical analysis.
A Holographic Augmented Reality Guidance System for Patient Alignment: A Feasibility Study

PurposeTo evaluate the accuracy of an augmented reality holographic guidance system for potential use in patient alignment in radiotherapy applications. MethodsA cubic phantom was scanned on a CT simulator and a 3D mesh was extracted using the Eclipse Scripting API.An application was created for the Microsoft HoloLens to allow users to see the scanned mesh as a hologram overlaid in the treatment vault.Six therapists were equipped with the HoloLens glasses and instructed to move the real phantom to align with the perceived spatial hologram using only couch controls.The initial couch coordinates were recorded and then recorded at each step as the therapist moved the phantom to each new location.The application varied the position of the virtual phantom to 10 preprogrammed locations within a 40-cm cubic volume in a combination of vertical, longitudinal, and lateral axis shifts.The absolute position difference between the holographic world and real-world phantom was recorded at each step.Also, the relative position from one position to the next was recorded. ResultsFifty shifts were collected across the six therapists.The mean difference between the physical position and instructed holographic position was 0.58 ± 0.31 cm for relative shifts and 0.51 ± 0.33 cm for absolute position.The maximum difference between the holographic position and the actual post shift position was 1.53 cm for relative and 1.58 cm for absolute. ConclusionHolographic augmented reality guidance using the Microsoft HoloLens provides adequate accuracy for initial treatment alignment but lacks the fine alignment accuracy of X-ray imaging systems.
TCP-Net: Minimizing Operation Counts of Binarized Neural Network Inference

Binarized neural network (BNN) dataflow inference accelerators have emerged as a promising solution to be applied in cost- and power-restricted domains, such as loT and smart edge- devices. However, there still exists abundant redundancies in BNN inference, which severely limit the performances of these accelerators. To alleviate the performance degradation, we propose TCP-Net, an efficient architecture to minimize the number of operations in BNN inference while maintaining the original accuracy. Inspired by the observation that the processes of obtaining the outputs of multiple related kernels in BNNs contain significant repeated calculations, we first build a formula to bridge these outputs by utilizing the kernel inclusion similarity and eliminate the unnecessary operations. Through the recursive algorithm, we further convert each original XNOR-popcount convolution into threshold-comparable-popcount (TCP) operations, which can be implemented to directly achieve the final output without any extra steps. Furthermore, we reduce the remaining TCP operation counts by exploiting tile-based pruning strategy. Compared to the prior state-of-the-art designs, TCP-Net saves 79.11 percent of the operations without any accuracy loss, bringing 6.0× inference-speedup and 12.4× energy-efficiency improvement.
Automated grapheme-to-phoneme conversion for Central Kurdish based on optimality theory

The writing system of Central Kurdish features three cases in which there is no one-to-one mapping between the orthographical letters and the phonemes of the language. Consequently, the written words including these cases may be pronounced in multiple ways. The process of finding the correct pronunciation of written words is called Grapheme-to-Phoneme (G2P) conversion and is a key step in natural language processing tasks such as speech synthesis. As Central Kurdish is a low-resourced language, we present a G2P conversion method based on the phonological rules of the language, rather than pronunciation dictionaries and data-driven learning methods. After reviewing the phonology and alphabet of the language through the framework of Optimality Theory, we generate all possible pronunciations. Then, by specifying and applying ranked constraints, we eliminate undesirable candidates so as to keep only one well-formed pronunciation per word. The evaluation of our proposed method on two datasets resulted in 0.75% of overall Phoneme Error Rate (PER) and achieved 94.71% precision in the detection of the short vowel /i/ and 100% of accuracy in the conversion of the letters “ی” and “و”. Analyzing these results suggests that there is no need for additional new letters in the current orthographic system of Central Kurdish. This approach also enables us to have a ranked suggestion list for the manual checking of the few unresolved ambiguous situations.
Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

In this paper, we consider a task of stopping the video stream recognition process of a text field, in which each frame is recognized independently and the individual results are combined together. The video stream recognition stopping problem is an under-researched topic with regards to computer vision, but its relevance for building high-performance video recognition systems is clear. Firstly, we describe an existing method of optimally stopping such a process based on a modelling of the next combined result. Then, we describe approximations and assumptions which allowed us to build an optimized computation scheme and thus obtain a method with reduced computational complexity. The methods were evaluated for the tasks of document text field recognition and arbitrary text recognition in a video. The experimental comparison shows that the introduced approximations do not diminish the quality of the stopping method in terms of the achieved combined result precision, while dramatically reducing the time required to make the stopping decision. The results were consistent for both text recognition tasks.