Reviewerly/exHarmony huggingface dataset:
Unsupervised Deep Learning for Just Noticeable Difference Estimation

Just noticeable difference (JND) estimates the visual redundancies of the human visual system (HVS), which has been widely applied in perceptual redundancy estimations in images and videos. Existing handcrafted feature based JND models are always inspired by some kind of HVS mechanisms, and have a limited performance for JND threshold estimation. Recently, deep learning has been widely used in various visual tasks and achieved notable success. However, deep learning is difficult to be applied for JND estimation, since it is impossible to build a large-scale dataset with pixellevel-label for training. In this paper, we propose an unsupervised learning based JND model. The underlying idea is to learn the visual redundancy characteristics of HVS by convolutional neural networks (CNN) without labeled data. Specifially, in order to optimize the parameters of the proposed model, three types of prior knowledge, i.e., the image quality, the pattern complexity, and the noise masking ability, are used to estimate the JND map of an image. Experiments demonstrate that the proposed model is highly consistent with the HVS and outperforms the existing JND models.
Evolution of Emacs Lisp

While Emacs proponents largely agree that it is the world’s greatest text editor, it is almost as much a Lisp machine disguised as an editor. Indeed, one of its chief appeals is that it is programmable via its own programming language. Emacs Lisp is a Lisp in the classic tradition. In this article, we present the history of this language over its more than 30 years of evolution. Its core has remained remarkably stable since its inception in 1985, in large part to preserve compatibility with the many third-party packages providing a multitude of extensions. Still, Emacs Lisp has evolved and continues to do so. Important aspects of Emacs Lisp have been shaped by concrete requirements of the editor it supports as well as implementation constraints. These requirements led to the choice of a Lisp dialect as Emacs’s language in the first place, specifically its simplicity and dynamic nature: Loading additional Emacs packages or changing the ones in place occurs frequently, and having to restart the editor in order to re-compile or re-link the code would be unacceptable. Fulfilling this requirement in a more static language would have been difficult at best. One of Lisp’s chief characteristics is its malleability through its uniform syntax and the use of macros. This has allowed the language to evolve much more rapidly and substantively than the evolution of its core would suggest, by letting Emacs packages provide new surface syntax alongside new functions. In particular, Emacs Lisp can be customized to look much like Common Lisp, and additional packages provide multiple-dispatch object systems, legible regular expressions, programmable pattern-matching constructs, generalized variables, and more. Still, the core has also evolved, albeit slowly. Most notably, it acquired support for lexical scoping. The timeline of Emacs Lisp development is closely tied to the projects and people who have shaped it over the years: We document Emacs Lisp history through its predecessors, Mocklisp and MacLisp, its early development up to the “Emacs schism” and the fork of Lucid Emacs, the development of XEmacs, and the subsequent rennaissance of Emacs development.
An Application of Backpropagation Neural Network for Sales Forecasting Rice Miling Unit

There are many rice fields and rice milling factories. Rice Milling Units (RMU) are still many who have not applied the prediction method for the sale of rice so that it can affect the availability of raw materials. The purpose of this research is to forecast product sales (rice) at RMU so that it can know the number of raw materials needed so that they avoid idle time. Obtain optimal forecasting; it compared to 2 (two) forecasting methods, Linear Regression, and Artificial Neural network with a Backpropagation algorithm. The results showed that the value of MSE on a linear regression method of 0.214, while at the time using Artificial Neural Network obtained an MSE value of 0.00099713. Based on the value of the MSE, the smallest MSE is forecasting by the Backpropagation Neural Network method.
A High-Resolution High Dynamic Range Light-Field Dataset with an Application to View Synthesis and Tone-Mapping

Light-field (LF) imaging has various advantages over the traditional 2D photography, providing angular information of the real world scene by separately recording light rays in different directions. Despite the directional light information which enables new capabilities such as depth estimation, post-capture refocusing, and 3D modelling, currently available light-field datasets are very restricted in terms of spatial-resolution and dynamic range. In this work, we address this problem by capturing a novel light-field dataset featuring both a high spatial resolution and a high dynamic range (HDR). This dataset should enable the community to research and develop efficient reconstruction and tone-mapping algorithms for a hyper-realistic visual experience. The dataset consists of six static light-fields that are captured by a high-quality digital camera mounted on two precise linear axes using exposure bracketing at each view point. To demonstrate the usefulness of such a dataset, we also performed a thorough analysis on local and global tone-mapping of natural data in the context of novel view-rendering. The rendered results are compared and evaluated both visually and quantitatively. To our knowledge, the recorded dataset is the first attempt to jointly capture high-resolution and HDR light-fields.
Problems of Using Solar Photovoltaic Panels and Ways to Increase their Efficiency

Developing various capacities and methods of installation of solar power plants is a promising sector of global electricity production today. The article studies the problem of reduced generation of electricity by solar power plants due to the pollution of solar panels with dust particles, atmospheric precipitation of industrial waste and biological pollution against the background of climatic operating conditions. The paper presents a calculation of catching dust particles efficiency depending on the air flow speed. The research results, presented in this article, should be taken into account in the solar power plants construction and operation, which will significantly increase power generation and reduce operating costs.
Querying of several DL-Lite knowledge bases from various information sources-based polynomial response unification approach

The inconsistency in the hierarchical knowledge base is due to the fact that the assertions (ABoxes) come from several sources with different levels of reliability. We introduce the management of this inconsistency problem to query the inconsistent DL-Lite knowledge bases. Most of the approaches studied in the literature first repair all of the inconsistent assertions in the DL-Lite incoherent knowledge base and then question it. In this article, we proceed directly to a knowledge base query to retrieve an exhaustive list of responses and then, once these responses are retrieved, we repair them. The main contribution of this work therefore consists of studying several response strategies to queries from various data sources using a recurrent function to calculate the rank of coherence which will allow us to manage inconsistencies in the set of responses. We study these strategies from different points of view: productivity, precision, recall, F-measure, Accuracy, G-mean and computational complexity. The experiments carried out, on different corpora, and the analysis of the results, which we carried out, show encouraging results in productivity since our approach is much more productive, providing the greatest number of responses, while remaining the best in time compared to the other studied approaches.
Deep Convolutional Likelihood Particle Filter for Visual Tracking

We propose a novel particle filter for convolutional-correlation visual trackers. Our method uses correlation response maps to estimate likelihood distributions and employs these likelihoods as proposal densities to sample particles. Likelihood distributions are more reliable than proposal densities based on target transition distributions because correlation response maps provide additional information regarding the target's location. Additionally, our particle filter searches for multiple modes in the likelihood distribution, which improves performance in target occlusion scenarios while decreasing computational costs by more efficiently sampling particles. In other challenging scenarios such as those involving motion blur, where only one mode is present but a larger search area may be necessary, our particle filter allows for the variance of the likelihood distribution to increase. We tested our algorithm on the Visual Tracker Benchmark v1.1 (OTB100) and our experimental results demonstrate that our framework outperforms the state-of-the-art methods.
Two‐group Poisson‐Dirichlet mixtures for multiple testing

The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severe diarrhea.
Improving interpretability of word embeddings by generating definition and usage

Word embeddings are substantially successful in capturing semantic relations among words. However, these lexical semantics are difficult to be interpreted. Definition modeling provides a more intuitive way to evaluate embeddings by utilizing them to generate natural language definitions of corresponding words. This task is of great significance for practical application and in-depth understanding of word representations. We propose a novel framework for definition modeling, which can generate reasonable and understandable context-dependent definitions. Moreover, we introduce usage modeling and study whether it is possible to utilize embeddings to generate example sentences of words. These ways are a more direct and explicit expression of embedding’s semantics for better interpretability. We extend the single task model to multi-task setting and investigate several joint multi-task models to combine usage modeling and definition modeling together. Experimental results on existing Oxford dataset and a new collected Oxford-2019 dataset show that our single-task model achieves the state-of-the-art result in definition modeling and the multi-task learning methods are helpful for two tasks to improve the performance.
Hierarchical neural network for hand pose estimation

Hand pose estimation plays an important role in human–computer interaction and augmented reality. Regressing the joints coordinates is a difficult task due to the flexibility of the joint, self-occlusion and so on. In this paper, we propose a novel and simple hierarchical neural network for hand pose estimation. The hand joint coordinates are divided into six parts and each part is regressed in sequence with this hierarchical architecture. This can divide the complex task of regressing all hand joints coordinates into several sub-tasks which can make the estimation more accurate. When regress the joint coordinates of one part, the features of other parts may bring negative influence to this part due to the similarity among the fingers, so we use an interference cancellation operation in our hierarchical architecture. At the time the joint coordinates of one part are regressed, the corresponding features will be removed from the hand global feature to eliminate the interference of this part. The obtained features will be used as input for regressing the joints coordinates of the next part. The ablation study verifies the effectiveness of our hierarchical architecture. The experimental results demonstrate that our method can achieve state-of-the-art or comparable results relative to existing methods on four public hand pose datasets.
Creating Semantic Representations

In this chapter, we present the vector space model and some ways to further process such a representation: With feature hashing, random indexing, latent semantic analysis, non-negative matrix factorization, explicit semantic analysis and word embedding, a word or a text may be associated with a distributed semantic representation. Deep learning, explicit semantic networks and auxiliary non-linguistic information provide further means for creating distributed representations from linguistic data. We point to a few of the methods and datasets used to evaluate the many different algorithms that create a semantic representation, and we also point to some of the problems associated with distributed representations.
Goal programming on optimal pairings selection from flight schedule using Bat Algorithm

Abstract Nowadays, the demands of flight service are very high so that flight industry should minimize operational cost such as crew cost. Crew cost depends on pairings from flight schedule. Optimization model of this problem is selecting optimal pairings covering all flight numbers in the goal programming model as multiobjective programming. In this research, goal programming on optimal pairings selection will be applied by heuristic method like Bat Algorithm (BA). BA is optimization method inspired from behavior of bats like they use sonar called echolocation to detect prey, avoid obstacles, and locate their roosting crevices in the dark. BA can be applied on constrained optimization. Simulations are applied by generating the set of pairings and selection using BA. The advantages of BA are there are a local solution around the selected best solution based on pulse rates and loudness. In the optimal pairings selection model, there are three objectives which will be minimized based on order of importance. The simulation results show that Bat Algorithm can be used in goal programming model and it can select optimal pairings in approaching with the number of pairing consisting of two flight numbers is 4 pairings, the number of pairing consisting of three flight numbers is 0 pairing, the number of pairing consisting of four flight numbers is 49 pairings, the number of pairing consisting of five flight numbers is 7 pairings and the number of pairing consisting of six flight numbers is 8 pairings.
Lower bound for succinct range minimum query

Given an integer array A[1..n], the Range Minimum Query problem (RMQ) asks to preprocess A into a data structure, supporting RMQ queries: given a,b∈ [1,n], return the index i∈[a,b] that minimizes A[i], i.e., argmin i∈[a,b] A[i]. This problem has a classic solution using O(n) space and O(1) query time by Gabow, Bentley, Tarjan (STOC, 1984) and Harel, Tarjan (SICOMP, 1984). The best known data structure by Fischer, Heun (SICOMP, 2011) and Navarro, Sadakane (TALG, 2014) uses 2n+n/(logn/t) t +Õ(n 3/4) bits and answers queries in O(t) time, assuming the word-size is w=Θ(logn). In particular, it uses 2n+n/polylogn bits of space as long as the query time is a constant.
A Comparative Study of Different Feature Descriptors for Video-Based Human Action Recognition

Human action recognition (HAR) has been a well-studied research topic in the field of computer vision since the past two decades. The objective of HAR is to detect and recognize actions performed by one or more persons based on a series of observations. In this paper, a comparative study of different feature descriptors applied for HAR on video datasets is presented. In particular, we estimate four standard feature descriptors, namely histogram of oriented gradients (HOG), gray-level co-occurrence matrix (GLCM), speeded-up robust features (SURF), and Graphics and Intelligence-based Scripting Technology (GIST) descriptors from RGB videos, after performing background subtraction and creating a minimum bounding box surrounding the human object. To further speed up the overall process, we apply an efficient sparse filtering method, which reduces the number of features by eliminating the redundant features and assigning weights to the features left after elimination. Finally, the performance of the said feature descriptors on three standard benchmark video datasets namely, KTH, HMDB51, and UCF11 has been analyzed.
Interactive Entity Linking Using Entity-Word Representations

To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. In this paper, we leverage on human intelligence for embedding-based interactive entity linking. We adopt an active learning approach to select mentions for human annotation that can best improve entity linking accuracy at the same time updating the embedding model. We propose two mention selection strategies based on: (1) coherence of entities linked, and (2) contextual closeness of candidate entities with respect to mention. Our experiments show that our proposed interactive entity linking methods outperform their batch counterpart in all our experimented datasets with relatively small amount of human annotations.
Harnessing the Symmetry of Convolutions for Systematic Generalisation

We argue that symmetry is an important consideration in addressing the problem of systematic generalisation and investigate two forms of symmetry relevant to symbolic processes. We implement this approach in terms of convolution and show that it can be used to achieve effective generalisation in a rule learning and a context free language task.In the rule learning task, we find that symmetry allows us to learn rules that abstract away from the particular symbols that instantiate them, enabling generalisation from seen to unseen symbols. In the language task, symmetry allows us to impose a stack like architecture on the memory cells of a recurrent net, which permits generalisation from simple to more complex structures.
Memory FORESHADOW: Memory FOREnSics of HArDware CryptOcurrency wallets – A Tool and Visualization Framework

We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the Ledger and Trezor hardware wallet clients. The framework can be generalized for use in future memory forensics work.
Joint Detection, Re-Identification, And Lstm In Multi-Object Tracking

Using Convolutional Neural Networks (CNN) in object tracking typically utilizes spatial features, while ignores the temporal correlation of frames in the whole film, causing that it is easy to lose the target when it is occluded by other objects. To cope with the problem, a robust system combining CNN and long short-term memory (LSTM) is proposed for multi-object tracking. The system consists of three modules: object detection, data association, and LSTM tracking. With the proposed approach, the tracking accuracy can be greatly improved especially when the tracking targets suffer from occlusion. Experimental results showed that the proposed system exhibits outstanding tracking accuracy and stability.
Offline Handwritten Signature Verification Based on Circlet Transform and Statistical Features

Handwriting signatures are widely used to register ownership in banking systems, administrative and financial applications, all over the world. With the increasing advancement of technology, increasing the volume of financial transactions, and the possibility of signature fraud, it is necessary to develop more accurate, convenient, and cost effective signature based authentication systems. In this paper, a signature verification method based on circlet transform and the statistical properties of the circlet coefficients is presented. Experiments have been conducted using three benchmark datasets: GPDS synthetic and MCYT-75 as two Latin signature datasets, and UTSig as a Persian signature dataset. Obtained experimental results, in comparison with literature, confirm the effectiveness of the presented method.
Decision Making Process in Keystroke Dynamics

Abstract Computer system intrusion often happens nowadays. Various methods have been introduced to reduce and prevent these intrusions, however no method was 100% proven to be effective. Therefore, to improve the computer’s security, this writing will explain the application of KD in the application system. The effectiveness of KD could not guarantee one hundred percent to prevent the computer intrusion, but it can be used as a second level of security after the login page in the application system. The pattern and time taken while typing by an individual is the core for the second level of security check after the login page. This writing will elaborate and conclude past studies related to KD on the aspects of decision-making process. Various methods of processing KD data that have been used are listed and the results of the study are compared. The results of this writing are expected to help new researchers in the process of evaluating KD data.
A case study on the effects and limitations of refactoring

We present design issues and empirical observations on extending an industrial development environment with a new refactoring tool for Feature Envy code smells. Refactoring is generally considered to improve code quality, however, it seems from multiple studies that such tools may introduce new bugs and are rarely used by the developers. We tried to discover through a case study why they are not used more often and how they could be made more useful in large scale development.We implemented a refactoring feature executing Move Method refactoring on TTCN-3 projects and performed a survey with industrial experts working in large scale software development projects.In our empirical research we found that the refactoring method preferred grants more control to the users. By doing so, additional factors can be considered while refactoring besides metrics, such as human factors and company policies.