Reviewerly/exHarmony huggingface dataset:
Algorithm 994

The minimum distance of an error-correcting code is an important concept in information theory. Hence, computing the minimum distance of a code with a minimum computational cost is crucial to many problems in this area. In this article, we present and assess a family of implementations of both the brute-force algorithm and the Brouwer-Zimmermann algorithm for computing the minimum distance of a random linear code over F 2 that are faster than current implementations, both in the commercial and public domain. In addition to the basic sequential implementations, we present parallel and vectorized implementations that produce high performances on modern architectures. The attained performance results show the benefits of the developed optimized algorithms, which obtain remarkable improvements compared with state-of-the-art implementations widely used nowadays.
A design and implementation of dual axis solar tracker using mini photo voltaic as solar sensor

This paper discusses the improved performance of Photo Voltaic (PV) panels in a solar energy harvesting. The proposed system is a system of tracking position of the sun in two axes. Mini photo voltaic used as sensors which will track the position of the sun based on the incoming Voltage. The voltage is read by a solar sensor to guide the movement of the actuator to track the position of the sun. The voltage sensor is added on the charging system. It functions to limit the minimum voltage for charging, according to the battery voltage This system has been tested through experimentation and can be concluded that the panel with dual axis tracking system is proven to increase the absorption of solar energy by up to 26% with gain energy stored in the battery is 66.39%, regarding static photovoltaic panels.
Assessing Texture Dimensions and Video Quality in Motion Pictures using Sensory Evaluation Techniques

The quality of images and videos is usually examined with well established subjective tests or instrumental models. These often target content transmitted over the internet, such as streaming or videoconferences and address the human preferential experience. In the area of high-quality motion pictures, however, other factors are relevant. These mostly are not error-related but aimed at the creative image design, which has gained comparatively little attention in image and video quality research. To determine the perceptual dimensions underlying movie-type video quality, we combine sensory evaluation techniques extensively used in food assessment - Degree of Difference test and Free Choice Profiling - with more classical video quality tests. The main goal of this research is to analyze the suitability of sensory evaluation methods for high-quality video assessment. To understand which features in motion pictures are recognizable and critical to quality, we address the example of image texture properties, measuring human perception and preferences with a panel of image-quality experts. To this aim, different capture settings were simulated applying sharpening filters as well as digital and analog noise to exemplary source sequences. The evaluation, involving Multidimensional Scaling, Generalized Procrustes Analysis as well as Internal and External Preference Mapping, identified two separate perceptual dimensions. We conclude that Free Choice Profiling connected with a quality test offers the highest level of insight relative to the needed effort. The combination enables a quantitative quality measurement including an analysis of the underlying perceptual reasons.
The Semantic Librarian: A search engine built from vector-space models of semantics

Psychologists have made substantial progress at developing empirically validated formal expressions of how people perceive, learn, remember, think, and know. In this article, we present an academic search engine for cognitive psychology that leverages computational expressions of human cognition (vector-space models of semantics) to represent and find articles in the psychological record. The method shows how psychological theory can be used to inform and aid the design of psychologically intuitive computer interfaces.
Crowdsourcing formulaic phrases: towards a new type of spoken corpus

Spoken corpora have traditionally been assembled through careful recording and transcription of discourse events, a process which is both labour intensive and often restrictive in terms of breadth of recording contexts available. To overcome these potential challenges in spoken corpus compilation, we explore the use of crowdsourcing of language samples that are reported by participants. We investigate the level of precision and recall of the ‘crowd’ when it comes to reporting language they have heard in certain contexts, alongside the use of a crowdsourcing toolkit to facilitate this task. As a focussing device for the selection of reported language samples, we draw on the use of formulaic phrases as an area that has received considerable attention by corpus linguists and applied linguists over the years. We argue that while studying reported language usage instead of actual language-in-use is problematic for several reasons, many of which have been highlighted in the literature on Discourse Completion Tasks ( Schauer and Adolphs, 2006 ), our suggested approach presents several advantages and opportunities for spoken corpus linguistics.
Radix Path: A Reduced Bucket Size ORAM for Secure Cloud Storage

This paper proposes a novel version of path oblivious random access memory called radix path ORAM (R-Path ORAM) with a large root (radix) bucket size but a small fixed size for all the other buckets in the tree. A detailed analysis of the root bucket occupancy is conducted to provide a closed-form solution of the required root bucket size that maintains a negligible failure probability. The performance of the R-Path ORAM is evaluated and compared against the traditional Path ORAM using a unified platform. The conducted experiments clearly show that R-Path ORAM provides much lower server storage and average response time than the seminal Path ORAM. Furthermore, we propose a background eviction technique to eventually reduce the root bucket size and avoid system failure. The conducted experiments on the unified platform showed the usefulness and efficiency of the proposed two-way eviction technique in successfully reducing the root bucket size while incurring a very small overhead.
Diagram Combinators in MMT

Formal libraries, especially large ones, usually employ modularity to build and maintain large theories efficiently. Although the techniques used to achieve modularity vary between systems, most of them can be understood as operations in the category of theories and theory morphisms. This yields a representation of libraries as diagrams in this category, with all theory-forming operations extending the diagram. However, as libraries grow even bigger, it is starting to become important to build these diagrams modularly as well, i.e., we need languages and tools that support computing entire diagrams at once. A simple example would be to systematically apply the same operation to all theories in a diagram and return both the new diagram and the morphisms that relate the old one to the new one. In this work, we introduce such diagram combinators as an extension to the MMT language and tool. We provide many important combinators, and our extension allows library developers to define arbitrary new ones. We evaluate our framework by building a library of algebraic theories in an extremely compact way.
Expression caching for runtime verification based on parameterized probabilistic models

Self-adaptive software systems change their behaviors to adapt to their environmental changes at runtime. Runtime verification, which checks the correctness of behaviors after adaptation, sometimes uses probabilistic model checking, because the verification has to deal with uncertainty. However, since probabilistic model checking is usually computation intensive and time consuming, a more efficient verification mechanism is desired. A possible approach is to pre-generate some expressions for model checking at design time and execute model checking simply by evaluating the expressions at runtime. A problem with this approach is that when environmental changes require changes of the system model, these expressions need to be re-generated at runtime. In order to cope with such significant changes, we develop a caching mechanism that reduces computational time at runtime. We also introduce a parameterization technique in order to improve the efficiency of caching. The experimental results show that our new implementation of the caching mechanism greatly improves the computational time of runtime verification.
Refinement of Background-Subtraction Methods Based on Convolutional Neural Network Features for Dynamic Background

Advancing the background-subtraction method in dynamic scenes is an ongoing timely goal for many researchers. Recently, background subtraction methods have been developed with deep convolutional features, which have improved their performance. However, most of these deep methods are supervised, only available for a certain scene, and have high computational cost. In contrast, the traditional background subtraction methods have low computational costs and can be applied to general scenes. Therefore, in this paper, we propose an unsupervised and concise method based on the features learned from a deep convolutional neural network to refine the traditional background subtraction methods. For the proposed method, the low-level features of an input image are extracted from the lower layer of a pretrained convolutional neural network, and the main features are retained to further establish the dynamic background model. The evaluation of the experiments on dynamic scenes demonstrates that the proposed method significantly improves the performance of traditional background subtraction methods.
A critical discourse analysis of the human flesh search engine

The crowd-sourcing phenomenon, known as the human flesh search (HFS) in China, is analyzed in this research through the lens of the critical discourse analysis (CDA). The article first introduces the HFS engine phenomenon and its controversies within the Chinese context characterized by government control, communal culture, and inchoate privacy law. This new form of peer-surveillance on social networking sites reveals an uneasy power relation between society and individuals. The crowd, often seen as manipulated or resisting the government in China, can also be seen as enacting the ideology of the elite. The study identifies themes and ideologies in discourses surrounding the HFS phenomenon in China Daily, The International Herald Tribune, and mop.com A comparative analysis of the legally and culturally fluid notions of privacy in China and in the US in this research shows that both the pejorative connotation of privacy and traditional communal cultural values have made their way into China’s new privacy laws. This research unveils the co-enactment of power by media, the public, and the state in China.
Cleaning StackOverflow for Machine Translation

Generating source code API sequences from an English query using Machine Translation (MT) has gained much interest in recent years. For any kind of MT, the model needs to be trained on a parallel corpus. In this paper we clean StackOverflow, one of the most popular online discussion forums for programmers, to generate a parallel English-Code corpus from Android posts. We contrast three data cleaning approaches: standard NLP, title only, and software task extraction. We evaluate the quality of the each corpus for MT. To provide indicators of how useful each corpus will be for machine translation, we provide researchers with measurements of the corpus size, percentage of unique tokens, and per-word maximum likelihood alignment entropy. We have used these corpus cleaning approaches to translate between English and Code [22, 23], to compare existing SMT approaches from word mapping to neural networks [24], and to re-examine the "natural software" hypothesis [29]. After cleaning and aligning the data, we create a simple maximum likelihood MT model to show that English words in the corpus map to a small number of specific code elements. This model provides a basis for the success of using StackOverflow for search and other tasks in the software engineering literature and paves the way for MT. Our scripts and corpora are publicly available on GitHub [1] as well as at https://search.datacite.org/works/10.5281/zenodo.2558551.
Kernel-based Fisher discriminant analysis on the Riemannian manifold for nuclear atypia scoring of breast cancer

Breast carcinoma is the most prevalent type of malignancy among women worldwide. Breast cancer grading often termed as Nuclear Atypia Scoring (NAS) forms a significant factor in determining individualized treatment plans and in the prognosis of the disease. For addressing the problem of breast cancer grading, we attempt to model the variations in features between histopathological images of different cancer grades and thereby explore the discriminative information concealed in these variations. In this regard, we aggregate multiple correlated features from the images using the geodesic geometric mean of the region covariances, to obtain the gmRC descriptors. As these gmRC descriptors are symmetric positive definite (SPD) matrices lying on the non-Euclidean Riemannian manifold, the discriminant analysis techniques developed for the Euclidean framework may not be appropriate. Hence, we propose a kernel-based Fisher discriminant analysis on the Riemannian manifold (KFDAR), that exploits the kernel trick for embedding the non-linear Riemannian manifold M into a higher dimensional linear Hilbert space H, which are then reduced to a low-dimensional and more discriminative subspace, where the samples become linearly separable. The kernel approach formulated for the Hilbert space embedding and for the kernel discriminant analysis is based on three Riemannian distance metrics: the log-Euclidean metric and the two symmetrized Bregman divergences – Stein and Jeffrey divergences. The experimental results show that this mapping to a highly discriminative space has succeeded in well-separating the histopathological images belonging to different cancer grades and hence it qualitatively and quantitatively outperforms the existing algorithms for cancer grading.
HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine

Extreme Learning Machine has attracted widespread attention for its exemplary performance in solving regression and classification problems. It is a type of single layer feed-forward neural machine which relies on randomly allocating the input weights and hidden layer biases. Through this, the ELM has been found to possess running time spans which are within millisecond regime. It does not require complex controlling parameters which makes its implementation elementary. This paper investigates the performance of employing Extreme Learning Machine as a classifier to be used for the face recognition problem. Viola Jones algorithm has been employed to detect and extract the faces from the dataset. Finally, Histogram of Oriented Gradients (HOG) features are extracted which form the basis of classification. The scheme so presented has been tested on standard face recognition datasets from AT&T and YALE. The resulting training/testing time spans of the whole scheme range from milliseconds to seconds, dictating the compatibility of ELM with real-time events.
Binarization of Degraded Document Images with Generalized Gaussian Distribution

One of the most crucial steps of preprocessing of document images subjected to further text recognition is their binarization, which influences significantly obtained OCR results. Since for degrades images, particularly historical documents, classical global and local thresholding methods may be inappropriate, a challenging task of their binarization is still up-to-date. In the paper a novel approach to the use of Generalized Gaussian Distribution for this purpose is presented. Assuming the presence of distortions, which may be modelled using the Gaussian noise distribution, in historical document images, a significant similarity of their histograms to those obtained for binary images corrupted by Gaussian noise may be observed. Therefore, extracting the parameters of Generalized Gaussian Distribution, distortions may be modelled and removed, enhancing the quality of input data for further thresholding and text recognition. Due to relatively long processing time, its shortening using the Monte Carlo method is proposed as well. The presented algorithm has been verified using well-known DIBCO datasets leading to very promising binarization results.
A 4K$\times$ 2K@60fps Multifunctional Video Display Processor for High Perceptual Image Quality

Video display processing is an indispensable module in a variety of multimedia systems. With the increase of video format to 4K×2K@60fps, the design of efficient display processing circuit faces severe challenges. This paper presents a video display processor which supports a variety of video formats and integrates multiple advanced algorithms for perpetual image quality improvement. By leveraging algorithm and architecture co-design, this paper efficiently implements these computational and memory intensive display processing algorithms in a single chip. In particular, we describe three representative design modules in detail, including motion compensated frame rate upconversion, content-adaptive contrast enhancement, and backlight local dimming. Extensive experiments demonstrate that the proposed design modules outperform the previous works in terms of either image quality or hardware efficiency. The overall chip is fabricated in GF 55nm CMOS technology, and can work at the maximum operating frequency of 594 MHz. The maximum input and output video formats reach up to 4K×2K@60fps.
Students’ Self-Awareness of Their Mathematical Thinking: Can Self-Assessment Be Supported Through CAS-Integrated Learning Apps on Smartphones?

A range of digital technology to support mathematics learning is freely available on the internet, particularly apps offering immediate access to different representations and calculations. In this article, we analyze some learning apps (available for smartphones) with an integrated computer algebra system (CAS) that offer support when learning how to solve equations. In the context of solving quadratic equations, use of apps in an informal way to learn how to solve not only touches on learning issues in the field of algebra, but also aspects of students’ self-regulation and the use of technology. These different aspects are discussed in the theoretical background and are used to guide our methodological approach to analyze different CAS-integrated learning apps.
Quantum hyper-computing by means of evanescent photons

Abstract Nowadays quantum computation is probably considered as the most promising way to achieve superfast calculation performances to be implemented in the next generation of hyper-computers. Nevertheless, the realization of an hypercomputer, namely a system able to perform countable infinite computational steps within a finite time, is usually considered not allowed due to the Heisenberg uncertainty principle that limits the energy required by such steps. This limitation affects quantum algorithms as well as conventional computation. In this paper we show it is possible to bypass such limitation by considering a quantum system making use of evanescent photons produced by some physical processes such, for example, that occurring inside optical components made of metamaterials. This proposal opens very interesting prospects towards a deeper understanding of quantum information and computation as well as to the realization of an actual hypercomputer system and its applications to frontier fields such as Artificial Intelligence.
Hybrid search plan generation for generalized graph pattern matching

In recent years, the increased interest in application areas such as social networks has resulted in a rising popularity of graph-based approaches for storing and processing large amounts of interconnected data. To extract useful information from the growing network structures, efficient querying techniques are required. In this paper, we propose an approach for graph pattern matching that allows a uniform handling of arbitrary constraints over the query vertices. Our technique builds on a previously introduced matching algorithm, which takes concrete host graph information into account to dynamically adapt the employed search plan during query execution. The dynamic algorithm is combined with an existing static approach for search plan generation, resulting in a hybrid technique which we further extend by a more sophisticated handling of filtering effects caused by constraint checks. We evaluate the presented concepts empirically based on an implementation for our graph pattern matching tool, the Story Diagram Interpreter, with queries and data provided by the LDBC Social Network Benchmark. Our results suggest that the hybrid technique may improve search efficiency in several cases, and rarely reduces efficiency.
Compression for DNA Sequences Using Huffman Encoding

Over the last two decades, the DNA sequence handling and storing problem has been considered as a big problem for many bioinformatics researchers because genomic databases are increasing drastically. To handle this problem, computational biology plays an important role, such as searching for homology, genome formulation, predicting for a new protein sequence, hereditary control networks, and new creative genomics structure. Available resources are not sufficient for storing and handling large DNA sequences. There are various tools developed by using different algorithms and approach. We have proposed an algorithm to improve the compression ratio which uses the concept of RLE (run length encoding) with a Modified HuffBit algorithm. The results obtained by the proposed method are found to be 20% more accurate when compared to existing algorithms.
An efficient vision based elderly care monitoring framework using fall detection

Fall is one of the major reason of elderly people living alone for getting injured. Numerous elderly individuals are living alone in their homes. In case if the elderly tumble down, it might be troublesome for them to ask for help. Due to this problem there is a dire requirement of an efficient home monitoring framework. An effective fall detection system can help provide rapid help to the patient and improve the chances of survival of the patient. The principle goal of this paper is to design a vision-based fall detection framework for the elderly people. Fall identification framework at home is progressively vital and dependable observation framework is a need to mitigate the outcomes of fall. In this paper, we look forward to detecting a fall and normal walking of an individual using background subtraction, Motion History Image, feature extractor like Zernike Moments and then using a learning model. The proposed framework is able to detect fall and normal walking with an accuracy of 92.86% and 90.82%.