Reviewerly/exHarmony huggingface dataset:
Human Face Detection Technique using Haar-like Features

Research on biometrics has become most popular topic nowadays.Face is one of the most important biometric traits that plays a crucial role in our social association, passing on individuals' identity.Face detection is very important and challenging topic in the field of biometric research.It is also an essential step of any face recognition system.The actual benefits of face-based recognition or identification are uniqueness and acceptance.Face recognition or identification also has distinct advantages over other biometric systems because of its non-contact process while capturing the facial image.Face images are captured from a distance without any touch to the person who is being identified and the entire identification process does not require interacting with the person.Face recognition is a method of identifying people throughout facial images and it has many practical applications in the area of biometrics, information security, access control, smart cards, law enforcement and surveillance system.Therefore, face detection/recognition is one of the most interesting and important topics in research field.The goal of this study is to explore the face detection system using conventional face detection techniques using Haar-like feature and Haar-cascade classifier using OpenCv library.An encouraging average accuracy of 95.16% was achieved in this experiment.
The Smallest Grammar Problem Revisited

In a seminal paper, Charikar et al. derive upper and lower bounds on the approximation ratios for several grammar-based compressors, but in all cases there is a gap between the lower and upper bound. Here the gaps for LZ78 and BISECTION are closed by showing that the approximation ratio of LZ78 is Θ((n/log n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2/3</sup> ), whereas the approximation ratio of BISECTION is Θ(√(n/log n)). In addition, the lower bound for RePair is improved from Ω(√(log n)) to Ω(log n/log log n). Finally, results of Arpe and Reischuk relating grammar-based compression for arbitrary alphabets and binary alphabets are improved.
Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses

Popular Neural Machine Translation model training uses strategies like backtranslation to improve BLEU scores, requiring large amounts of additional data and training. We introduce a class of conditional generative-discriminative hybrid losses that we use to fine-tune a trained machine translation model. Through a combination of targeted fine-tuning objectives and intuitive re-use of the training data the model has failed to adequately learn from, we improve the model performance of both a sentence-level and a contextual model without using any additional data. We target the improvement of pronoun translations through our fine-tuning and evaluate our models on a pronoun benchmark testset. Our sentence-level model shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets, while our contextual model achieves the best results, improving from 31.81 to 32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En testset, with corresponding improvements in pronoun translation. We further show the generalizability of our method by reproducing the improvements on two additional language pairs, Fr-En and Cs-En.
The ILLTP Library for Intuitionistic Linear Logic

Benchmarking automated theorem proving (ATP) systems using standardized problem sets is a wellestablished method for measuring their performance.However, the availability of such libraries for non-classical logics is very limited.In this work we propose a library for benchmarking Girard's (propositional) intuitionistic linear logic.For a quick bootstrapping of the collection of problems, and for discussing the selection of relevant problems and understanding their meaning as linear logic theorems, we use translations of the collection of Kleene's intuitionistic theorems in the traditional monograph "Introduction to Metamathematics".We analyze four different translations of intuitionistic logic into linear logic and compare their proofs using a linear logic based prover with focusing.In order to enhance the set of problems in our library, we apply the three provability-preserving translations to the propositional benchmarks in the ILTP Library.Finally, we generate a comprehensive set of reachability problems for Petri nets and encode such problems as linear logic sequents, thus enlarging our collection of problems.
Syntax-driven Iterative Expansion Language Models for Controllable Text Generation

The dominant language modeling paradigm handles text as a sequence of discrete tokens.While that approach can capture the latent structure of the text, it is inherently constrained to sequential dynamics for text generation.We propose a new paradigm for introducing a syntactic inductive bias into neural text generation, where the dependency parse tree is used to drive the Transformer model to generate sentences iteratively.Our experiments show that this paradigm is effective at text generation, with quality between LSTMs and Transformers, and comparable diversity, requiring less than half their decoding steps, and its generation process allows direct control over the syntactic constructions of the generated text, enabling the induction of stylistic variations.
Using Visual Feature Space as a Pivot Across Languages

Our work aims to leverage visual feature space to pass information across languages.We show that models trained to generate textual captions in more than one language conditioned on an input image can leverage their jointly trained feature space during inference to pivot across languages.We particularly demonstrate improved quality on a caption generated from an input image, by leveraging a caption in a second language.More importantly, we demonstrate that even without conditioning on any visual input, the model demonstrates to have learned implicitly to perform to some extent machine translation from one language to another in their shared visual feature space.We show results in German-English, and Japanese-English language pairs that pave the way for using the visual world to learn a common representation for language.
Motion Recognition Algorithm in VR Video Based on Dual Feature Fusion and Adaptive Promotion

VR video recognition in complex environment, a motion recognition algorithm based on two-feature fusion and adaptive enhancement is proposed to solve the problems of inaccurate target position, target drift and recognition error caused by the vulnerability to light change, target rotation and occlusion. First, based on the spatio-temporal context (STC) mechanism, image sequence features are extracted through spatio-temporal context relationship and visual system characteristics to reduce the influence of light changes and occlusion on behaviors. Secondly, reliable feature point tracks are obtained through image feature point tracking and background track cutting, and a rich set of action descriptors (AD) are calculated from which local motion information, shape and static appearance information of the track are retained. After that, the principal component analysis operator is introduced to define the double feature fusion rules, and the STC feature and AD feature are combined to form a more accurate and complete feature representation. Finally, adaptive boosting algorithm (ABA) is used to train the classification through the new features obtained and complete the decision judgment of behavior and action. The experimental results show that the proposed algorithm has higher recognition accuracy and robustness compared with the current commonly used motion recognition methods, and can better adapt to complex background and motion changes.
Interferobot: aligning an optical interferometer by a reinforcement learning agent

Limitations in acquiring training data restrict potential applications of deep reinforcement learning (RL) methods to the training of real-world robots. Here we train an RL agent to align a Mach-Zehnder interferometer, which is an essential part of many optical experiments, based on images of interference fringes acquired by a monocular camera. The agent is trained in a simulated environment, without any hand-coded features or a priori information about the physics, and subsequently transferred to a physical interferometer. Thanks to a set of domain randomizations simulating uncertainties in physical measurements, the agent successfully aligns this interferometer without any fine tuning, achieving a performance level of a human expert.
Hash Functions for GPU Rendering

Work on this project was funded in part by a gift from Epic Games. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS-0821258, CNS-1228778, and OAC-1726023) and the SCREMS program (grant no. DMS-0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.
Formal inferences and their relationships to knowledge acquisition: mental models and semantic links

The mental model theory is an approach with clear psychological, linguistic, and cognitive consequences. This paper delves into some of the epistemological conclusions that can be drawn from it. In particular, it addresses the process why knowledge acquisition can modify the inferences people tend to make. That process is described by means of an example based on a well-known logical schema related to the conditional: Modus Tollendo Tollens.
Relational Contexts and Conceptual Model Clustering

In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason with their content. In other words, it is important for these reference conceptual models to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages the rich semantics of ontology-driven conceptual models (ODCM). In particular, the technique employs the notion of Relational Context to guide automated model breakdown. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator).
Mining Knowledge for Natural Language Inference from Wikipedia Categories

Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations.To alleviate the need for labeled data, we introduce WIKINLI: a resource for improving model performance on NLI and LE tasks.It contains 428,899 pairs of phrases constructed from naturally annotated category hierarchies in Wikipedia.We show that we can improve strong baselines such as BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) by pretraining them on WIKINLI and transferring the models on downstream tasks.We conduct systematic comparisons with phrases extracted from other knowledge bases such as WordNet and Wikidata to find that pretraining on WIKINLI gives the best performance.In addition, we construct WIKINLI in other languages, and show that pretraining on them improves performance on NLI tasks of corresponding languages.
Macroscopic approach to N-qudit systems

We develop a general scheme for an analysis of macroscopic N-qudit systems which includes: (a) a scheme to organize the information obtained from collective measurements in the form of distribution functions in a discrete low-dimensional space; (b) a set of collective operators appropriate for the characterization of N-qudit states; (c) two collective tomographic protocols both for general and fully symmetric N-qudit states. The example of N-qutrits is analyzed in detail and compared to the N-qubit case.
LCNet: A Light-Weight Network for Object Counting

In recent years, the accuracy of object counting has been greatly improved by applying convolutional neural networks to generate density maps. However, most researchers do not take into consideration the running speed of models but the accuracy of models. The improvement of performance comes at the expense of a large amount of computations, which makes it difficult for models to run on edge devices with a low computing capacity. In this paper, we propose a light-weight but accurate neural network for object counting, called light-weight counting network (LCNet). Our proposed method is a fully convolutional network that contains a small number of parameters and can be trained in an end-to-end way. LCNet adopts a dilated convolution to increase the receptive field and uses a ghost module to further compress the number of network parameters. The performance on the three datasets shows that LCNet achieves the best balance between speed and accuracy. Our method is competent for both sparse and congested scenes. In addition, LCNet can obtain a higher running speed on both GPU and CPU, which is more suitable for edge devices.
The effect of knowledge management maturity on managerial flexibility: an empirical study in telecommunications companies in Jordan

While existing literature has generally concentrated on knowledge management maturity (KMM) and managerial flexibility, less focus has been dedicated to the effect of the former on the latter. Consequently, this study suggested a model of the effects of KMM dimensions: people, process, and technology on managerial flexibility dimensions: structural flexibility, and strategic flexibility. The responses of 168 employees working in mobile communications companies in Jordan engaged in managerial and specialised knowledge work were analysed to test the proposed research hypotheses. The results revealed that only people maturity, and process maturity have significant effects on the structural flexibility; whereas all the independent variables: people maturity, process maturity, and technology maturity have significant effects on the strategic flexibility. In the light of the results reached some practical implications for the explored organisations and proposed future studies have been recommended.
Decentralized Application for the Classification of Hotels Based on IPFS and Blockchain

This paper presents the results of the construction of a prototype that provides a web platform to classify and recommend hotels, in an unstructured format, specifically using images. The application uses the information supplied by the image repository that communicates with the platform. Using technologies such as blockchain and the Interplanetary File System (IPFS), it is intended to provide a new approach that allows users to use the benefits of these technologies to provide security on the quality of stored information and on content access.
Image Redundancy Filtering for Panorama Stitching

In this paper, we designed a novel framework for massive image panorama stitching, which aims to resolve image redundancy, alignment error accumulation and perspective distortion accumulation of the stitching process.First, an iterative method is designed to filter the redundancy images by analysing the similarity relation of adjacent images.Second, to reduce alignment error accumulation, the weight topology graph of filtered images is constructed, which is employed to find the optimal reference image which closes to the central geometrically by the Floyd-Warshall algorithm.Finally, a two-step global alignment strategy is designed to initial align images and perform shape optimization, the first step is that filtered images employ the similarity model to roughly align group by group, the second step is to perform shape optimization further through refining all alignment parameters by the homography model under the anti-perspective energy term, which aims to obtain an optimal solution by balancing the alignment accuracy and the global consistency.Compared with the state-of-the-art methods, the proposed method successfully reduces image redundancy while improving the alignment and reducing perspective distortion for massive image panorama stitching.
Radiation trapping effect versus superradiance in quantum simulation of light-matter interaction

We propose a realization of two remarkable effects of Dicke physics in quantum simulation of light-matter many-body interactions with artificial quantum systems. These effects are a superradiant decay of an ensemble of qubits and the opposite radiation trapping effect. We show that both phenomena coexist in the crossover regime of a "moderately bad" single-mode cavity coupled to the qubit subsystem. Depending on the type of the initial state and on the presence of multipartite entanglement in it, the dynamical features can be opposite resulting either in the superradiance or in the radiation trapping despite of the fact that the initial state contains the same number of excited qubits. The difference originates from the symmetrical or nonsymmetrical character of the initial wave function of the ensemble, which corresponds to indistinguishable or distinguishable emitters. We argue that a coexistence of both effects can be used in dynamical quantum simulators to demonstrate realization of Dicke physics, effects of multipartite quantum entanglement, as well as quantum interference and thus to deeply probe quantum nature of these artificial quantum systems.
Towards Understanding Modern Multi-Language Software Systems

RESUME : Aujourd’hui, la plupart des applications et sites Web tels que Google et Facebook sont des systemes multi-langages. Google est code en C, C++, Go, Java, Python et JS. Facebook, quant a lui, utilise Hack, PHP, Python, C++, Java, Erlang, D, Haskell et JS. De plus, de precedentes etudes ont permis de constater que les developpeurs PHP utilisaient regulierement deux langages en plus du PHP. Les developpeurs Java utilisent egalement le C/C++ avec du code Java a travers la Java Native Interface (JNI) qui permet d’appeler des fonctions natives a partir de methodes Java et des methodes Java a partir des fonctions natives. De plus, les developpeurs d’applications Android preferent utiliser Android NDK (qui permet d’utiliser du code C/C++ avec Android) en plus de Java, plutot que d’utiliser uniquement Java. Dans tous ces exemples, on observe le phenomene du developpement multi-langage. Bien qu’il y ait, le plus souvent, un seul langage principal (Java ou C/C++) avec divers contributions dans d’autres langages (par exemple, bash ou make), les logiciels modernes sont de plus en plus heterogenes dans le sens ou ils combinent de multiples langages de programmation qui interagissent d’une facon significative avec le langage principal.----------ABSTRACT : Today, most popular applications and websites such as Google and Facebook are multilanguage systems. Google is developed with C, C++, Go, Java, Python, and JS, while Facebook is using Hack, PHP, Python, C++, Java, Erlang, D, Haskell, and JS. Furthermore, previous research studies reported that PHP developers regularly use two languages besides PHP. Java developers also use C/C++ with Java code through the Java native interface (JNI) that allows to call native functions from Java methods and Java methods from native functions. Moreover, Android application developers prefer to use the Android NDK (allows to use C/C++ code with Android) along with Java compared to using only Java. In all these examples, we observe the phenomenon of multi-language development. While in many cases, there is one clear main language (e.g., Java or C/C++) with various smaller contributions from other languages (e.g., bash or make), increasingly more modern software are heterogeneous in the sense that they are composed of multiple programming languages that interact with the host language to a large extent.
INITIAL STEP OF SPECIALIZED CORPORA BUILDING: CLEANING PROCEDURES

This paper deals with the specialized corpora building, specifically academic language corpus in the biotechnology field. Being a part of larger research devoted to creation and usage of specialized parallel corpus, this piece aims to analyze the initial step of corpus building. Our main research question was what procedures we need to implement to the texts before using them to develop the corpus. Analysis of previous research showed the significant quantity of papers devoted to corpora creation, including academic specialized corpora. Different sides of the process were analyzed in these researches, including the types of texts used, the principles of crawling, the recommended length of texts etc. As to the text processing for the needs of corpora creation, only the linguistic annotation issues were examined earlier. At the same time, the preliminary cleaning of texts before their usage in corpora may have significant influence on the corpus quality and its utility for the linguistic research. In this paper, we considered three small corpora derived from the same set of academic texts in the biotechnology field: “raw” corpus without any preliminary cleaning and two corpora with different level of cleaning. Using different Sketch Engine tools, we analyzed these corpora from the position of their future users, predominantly as sources for academic wordlists and specialized multi-word units. The conducted research showed very little difference between two cleaned corpora, meaning that only basic cleaning procedures such as removal of reference lists are can be useful in corpora design. At the same time, we found a significant difference between raw and cleaned corpora and argue that this difference can affect the quality of wordlists and multi-word terms extraction, therefore these cleaning procedures are meaningful. The main limitation of the study is that all texts were taken from the unique source, so the conclusions could be affected by this specific journal’s peculiarities. Therefore, the future work should be the verification of results on different text collections
CHARACTERISTICS OF AN EFFECTIVE EFL TEACHER: PERSPECTIVES OF SLOVAK EFL STUDENTS AND TEACHERS

Teachers, scholars, and researchers have always been interested in examining the qualities of good and effective teaching and this type of investigation appears to be beneficial for both EFL teachers and learners. In line with those investigations, this study attempts to explore the Slovak EFL students’ and teachers’ perceptions of an effective EFL teacher. To achieve the research objectives, a convenient sample of Slovak EFL students (n = 45) and teachers (n = 32) were employed. An open-ended question questionnaire was created and distributed in order to gather the data. The research results indicate that there exists some agreement between the two targeted groups, but also some discrepancies between the students and teachers as regards their perception of effective EFL teaching. Further investigation into this is necessary to shed more light on this matter. Article visualizations:
Inferring symmetry in natural language

We present a methodological framework for inferring symmetry of verb predicates in natural language.Empirical work on predicate symmetry has taken two main approaches.The feature-based approach focuses on linguistic features pertaining to symmetry.The contextbased approach denies the existence of absolute symmetry but instead argues that such inference is context dependent.We develop methods that formalize these approaches and evaluate them against a novel symmetry inference sentence (SIS) dataset comprised of 400 naturalistic usages of literature-informed verbs spanning the spectrum of symmetryasymmetry.Our results show that a hybrid transfer learning model that integrates linguistic features with contextualized language models most faithfully predicts the empirical data.Our work integrates existing approaches to symmetry in natural language and suggests how symmetry inference can improve systematicity in state-of-the-art language models.