Reviewerly/exHarmony huggingface dataset:
A Continuous Space Neural Language Model for Bengali Language

Language models are generally employed to estimate the probability distribution of various linguistic units, making them one of the fundamental parts of natural language processing. Applications of language models include a wide spectrum of tasks such as text summarization, translation and classification. For a low resource language like Bengali, the research in this area so far can be considered to be narrow at the very least, with some traditional count based models being proposed. This paper attempts to address the issue and proposes a continuous-space neural language model, or more specifically an ASGD weight- dropped LSTM language model, along with techniques to efficiently train it for Bengali Language. The performance analysis with some currently existing count based models illustrated in this paper also shows that the proposed architecture outperforms its counterparts by achieving an inference perplexity as low as 51.2 on the held out data set for Bengali.
Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis

Architectural distortion (AD) is one of the most important potentially ominous signs of breast cancer. As a 3D imaging, digital breast tomosynthesis (DBT) is an accurate tool to detect AD. We developed a deep learning approach for AD detection guided by mammary gland spatial pattern (MGSP) in DBT. The approach consists of two stages: 2D detection and 3D aggregation. In 2D detection, prior MGSP information is obtained first. It includes 1) magnitude image and orientation field map produced from Gabor filters and 2) mammary gland convergence map. Second, Faster-RCNN detection network is employed. Region proposal network extracts features and determines locations of AD candidates and the soft classifier is used for reducing false positives. In 3D aggregation, a region fusion strategy is designed to fuse 2D candidates into 3D candidates. For evaluation, 265 DBT volumes (138 with ADs and 127 without any lesion) were collected from 68 patients. Free response receiver operating characteristic curve was obtained and the mean true positive fraction (MTPF) was used as the figure-of-merit of model performance. Compared with a baseline model based on convergence measure, the six-fold cross validation results showed that our proposed approach achieved MTPF of 0.50 &plusmn; 0.04, while the baseline achieved 0.37 &plusmn; 0.03. The improvement of our approach was statistically significant (p≪0.001).
Effect of virtual reality on motion sickness-An experimental Study

Background: Motion sickness occurs due to mismatch sensory inputs between the visual and vestibular system.Vestibular and visual systems coordinate eye movement in order to stabilize retinal images during head rotation.When someone is in stationary position they can still get the impression of self motion under certain conditions known as vection.Aim: To find out the effect of virtual reality on motion sickness.Methodology: It was an experimental study of pre-post design.Total 40 subjects participated in the study.Subjects who experience motion sickness of score more than 3 in modified Borg rating scale for dizziness were included in the study.Virtual Reality headset was used to deal with the problem of motion sickness by habituating visual vestibular system to induce adaptation in the VOR.Frequency of intervention was 3 sessions lasting 6 minutes each for 2 weeks.Dizziness score recorded after every session.Result: Repeated measures ANOVA showed that that virtual reality habituation lead to slight reduction in dizziness scoring from day 1 to day 11 (p < 0.001). Conclusion:Result of the study concluded that Virtual Reality headset showed improvement in symptoms of motion sickness as measured through modified borg rating scale for dizziness.
Lightweight Countermeasure to Differential-Plaintext Attacks on Permutation Ciphers

Many lightweight permutation based block ciphers have emerged for the use of encryption and security in the Internet of Things (IoT). However, recent work has shown a critical vulnerability in these ciphers due to the employment of static permutation networks in their architectures; Side-Channel Analysis and Differntial-Plaintext Attack (SCADPA) can be effectively performed on any cipher utilizing permutation networks. In this work, we present a lightweight solution for combating SCADPA. We demonstrate how this countermeasure can be employed on existing ciphers using the lightweight IoT PRESENT-80 cipher, providing multiple architectural implementations, and comparing the performance of these modified architectures against the unaltered PRESENT-80 cipher. Additionally, we analyze how this countermeasure impacts the resilience for all permutation ciphers when considering this attack scheme and provide alternative implementations and possible enhancements.
Study on non-phase-shifting phase retrieval methods for interferogram with large phase gradient

As a non-contact, high-precision optical component testing method, interferometry performs the measurement by solving the interferogram containing the phase information of the object to be measured. The non-mechanical-phase-shifting phase retrieval method based on single or two interferograms has the advantages of good vibration resistance, fast speed and simple system, which can meet the increasingly urgent online and on-site detection requirements. Aiming at the large gradient phase detection requirements in processing, this paper compares the accuracy of typical non-mechanical-phaseshifting interferometric phase retrieval methods, including carrier Fourier method, digital moiré interferometry (DMI), and two DMI-based methods, namely two-step carrier stitching method and DMI combined with Newton iterative algorithm proposed by the authors. In this paper, the influences of the magnitude of the phase gradient and the noise of the interferogram on the phase retrieval accuracy of the above methods are analyzed. The simulation results in this paper will provide a theoretical basis for the selection of algorithms for in situ interferometry.
An Active Learning Based Emoji Prediction Method in Turkish

Emoji usage has become a standard in social media platforms since it can condense feelings beyond short textual information.Recent advances in machine learning enable to write short messages with automatically detected emojis.However, the prediction of emojis for the given short message can be complicated, inasmuch as users can interpret different meanings beyond the intent of their designers.Therefore, an automatic extraction strategy of training samples cannot be convenient from the large volumes of unlabelled tweets.In this paper, we present an active learning method to evaluate the emoji prediction of a tweet with a limited number of labelled Turkish emoji dataset.To simulate a human-machine collaborative learning method, we train an initial classifier with this dataset and then we update the classifier by filtering related samples out from the large pool of unlabelled data.In the evaluation, we split 25% randomly selected tweets combined with only one emoji from the generated dataset as a test case.Our active learning method has achieved 0.901 F1 score and outperforms other baseline supervised learning methods.
Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Vehicles generate dissimilar sound patterns under different working environments.These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults.In this paper, the sound signals produced by motorcycles are analyzed to locate various faults.The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals.Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features.Moreover, the improved classification performance is obtained by the combination of feature sets in different domains.The simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.
Dynamic-attention based Encoder-decoder model for Speaker Extraction with Anchor speech

Speech plays an important role in human-computer interaction. For many real applications, an annoying problem is that speech is often degraded by interfering noise. Extracting target speech from background interference is a meaningful and challenging task, especially when interference is also human voice. This work addresses the problem of extracting target speaker from interfering speaker with a short piece of anchor speech which is used to obtain the target speaker identify. We propose a encoder-decoder neural network architecture. Specifically, the encoder transforms the anchor speech to a embedding which is used to represent the identity of target speaker. The decoder utilizes the speaker identity to extract the target speech from mixture. To make a acoustic-related speaker identity, The dynamic-attention mechanism is utilized to build a time-varying embedding for each frame of the mixture. Systematic evaluation indicates that our approach improves the quality of speaker extraction.
Non solo Berlusconi: rassegna panoramica dei populisti italiani della «Seconda Repubblica». Una prospettiva storico-linguistica

The article discusses the issue of populism and populists of the Italian political scene of the 2 nd Republic.In the introductory part of the paper the author aims to provide an explanation of the term "populism" in both etymologic and semantic perspectives (from linguistic and historic view).Subsequently, a selected overview of Italian populists of the 2 nd Republic shall be highlighted (as well as the term itself shall be made clear).They were chosen following a temporary criterion: among politicians who held public offices (ministers/members of Parliament/leaders of the governing and opposition parties) in the first decades after the Tangentopoli scandal and still take active part in public life.Their words coming out from their most popular speeches are confronted and analyzed in terms of language means applied to obtain the effect of shock, outrage or derision towards a political adversary.In conclusion, the function of message conveyed by a politician shall be emphasized, which differs depending on the model to be followed, not the ideological beliefs.
Robust Camera Model Identification Based on Richer Convolutional Feature Network

Based on convolutional neural network (CNN), the problem of robust patch level camera model identification is studied in this paper. Firstly, an effective feature representation is proposed by concatenating a multiscale residual prediction module as well as the original RGB images. Motivated by exploration of multi-scale characteristic, the multiscale residual prediction module automatically learn the residual images to avoid the subsequent CNN being affected by the scene content. Color channel information is integrated for enhanced diversity of CNN inputs. Secondly, a modified richer convolutional feature network is presented for robust camera model identification by fully exploiting the learnt features. Finally, the effectiveness of the proposed method is verified by abundant experimental results at the patch level, which is more difficult than image level experiments.
A Study on Transmission Overhead of Post Quantum Cryptography Algorithms in Internet of Things Networks

Post-quantum cryptography is an emerging solution to the expected security breach, introduced by quantum computers, to the currently used public key cryptosystems. This paper presents an experimental study on the effect of the overhead associated with the wireless transmission of some of the newly developed post quantum cryptography algorithms through the Internet of Things networks. Experimental results of this work include the energy and time measurements for wireless transmission of chosen sample messages of these new cryptographic algorithms. In addition, recommendations are given for appropriate modules configurations and choice of suitable security algorithm for the Internet of Things networks.
Bidirectional Temporal Convolution with Self-Attention Network for CTC-Based Acoustic Modeling

Connectionist temporal classification (CTC) based on recurrent (RNNs) or convolutional neural networks (CNNs) is a method for end-to-end acoustic modeling. Inspired by the recent success of the self-attention network (SAN) in machine translation and other domains such as images, we apply the SAN to CTC acoustic modeling in this paper. SAN has powerful capabilities for capturing global dependencies, but it cannot model the sequential information and local interactions of utterances. The bidirectional temporal convolution with self-attention network (BTCSAN) is proposed in order to capture both the global and local dependencies of utterances. Furthermore, the down- and upsampling strategies are adopted in the proposed BTCSAN in order to achieve computational efficiency and high recognition accuracy. Experiments are carried out using the King-ASR-117 Japanese corpus. The proposed BTCSAN can obtain a 15.87% relative improvement in the CER over the BLSTM-based CTC baseline.
Coincidence counts and stimulated emission resulting from weak pulsed-field–atom interactions

The interaction of a single-photon pulse of radiation with an atom or an ensemble of atoms is studied using a source-field approach. The atom-field interaction is weak insofar as it can be treated in lowest, nonvanishing order of perturbation theory. The output field intensity and second-order correlation function of the field are calculated. It is shown that, even when any modification of the atomic dynamics produced by the incident field is neglected, as a function of the time delay $\ensuremath{\tau}$ between the incident field pulse and the field radiated by the atom(s), there is a ``bump'' in the time-integrated second-order correlation at $\ensuremath{\tau}=0$. The increase in coincidence counts for $\ensuremath{\tau}=0$ can be interpreted in terms of Hanbury Brown and Twiss--type interference. By looking at the field intensity, we show that the bump has no direct relation with stimulated emission---it occurs even when the input field is attenuated. In recent down conversion experiments, an increase in coincidence counts has been attributed to stimulated emission---we comment on the validity of such an interpretation in light of our results.
Factors of Providing the Continuity of Teaching Mathematics During Transition from High School to University

The article deals with a current problem of providing the continuity of teaching mathematics during students' transition from high school to university.Today, there are a lot of scientific researches dealing with the continuity of education, which show the interest of scientists to this topic.Therefore, available researches were analyzed, summarized and systemized.The conclusion from this analyzation is that in most studies, scientists distinguish pedagogical, didactic, methodical, organizational conditions for providing continuity in the educational process.In our research, all these conditions were grouped into 6 blocks: content-related, organizational, methodical, instrumental, evaluative, social-psychological.According to the blocks, a questionnaire was developed.It contained 42 questions.The answer to each question implied that the respondent made a judgment on a scale of 1 to 5 points.Students of 1-3 courses of the Physics and Mathematics Faculty of the pedagogical university participated in the survey.The empirical data obtained were processed using the IBM SPSS Statistics 20, computer statistical package.A factor analysis method was chosen to analyze the data, because it reduces the large number of variables to fewer independent influential values -factors.The article describes in detail the algorithm for working with data.The results of the factor analysis were interpreted.The conducted research allowed us to define four factors that influence the process of providing the continuity of mathematics teaching.
Adversarial CNN Training Under JPEG Laundering Attacks: a Game-Theoretic Approach

Detecting image manipulations in the presence of JPEG post-processing is often a challenging task. For this reason, in many cases, JPEG compression is applied as a laundering-type counter-forensic attack, to impair the performance of the detector. A possible countermeasure, for data-driven approaches, consists in building a JPEG-aware detector by training the detector on JPEG-compressed images. However, the choice of the quality factor (QF) used to compress the training images is not obvious, given that the detector does not know the QF used by the attacker. In this work, we introduce a game-theoretic framework to compare three possible approaches to make such a choice, namely: i) training on images compressed using a single QF chosen strategically by taking into account the different goals of the forensic analyst and the attacker, ii) estimation of the QF used by the attacker and subsequent use of a network trained on a matched QF, iii) training on a mixture of QFs. We exemplify the proposed framework by applying it to the detection of a specific image manipulation, namely Adaptive Histogram Equalization (AHE). The experiments show how the proposed framework allows to design a JPEG-aware AHE-detector and set the stage for the use of a similar procedure for the detection of other image manipulations in the presence of JPEG-laundering attacks.
An adaptation of Text2Onto for supporting the French language

The ontologies are progressively imposing themselves in the field of knowledge management. While the manual construction of an ontology is by far the most reliable, this task has proved to be too tedious and expensive. To assist humans in the process of building an ontology, several tools have emerged proposing the automatic or semi-automatic construction of ontologies. In this context, Text2Onto has become one of the most recognized ontology learning tools. The performance of this tool is confirmed by several research works. However, the development of this tool is based on Princeton WordNet (PWN) for English. As a result, it is limited to the processing of textual resources written in English. In this paper, we present our approach based on JWOLF, a Java API to access the free WordNet for French that we have developed to adapt this tool for the construction of ontologies from corpus in French. To evaluate the usefulness of our approach, we assessed the performance of the improved version of Text2Onto on a simplistic corpus of French language documents. The results of this experiment have shown that the improved version of Text2Onto according to our approach is effective for the construction of an ontology from textual documents in the French language.
Correlations Between Self-Esteem And Sociometric Status In Information And Network Society

New revolutionary changes in information have resulted in a so-called network society that places great emphasis on one`s ability to transform society and its material environment. The degree of successful transformation is closely tied to one`s self-esteem, which is formed based on interpersonal relationships and personal sociometric status in the community. The purpose of the study was to determine correlations between one`s self-esteem and sociometric status at the earliest stages of their development in early adolescence. Methodologically, the study was based on works by foreign and domestic researchers and the age periodization theory. The following methods were applied in the study: the self-assessment of the mental states method after Hans Eysenck, the sociometry method by Jacob Moreno, and some methods of the mathematical and statistical analysis, in particular, Spearman's correlation analysis. The study revealed close correlations between a young teenager`s self-esteem and sociometric status. Moreover, it was found that increased sociometric status may result in higher self-esteem. The theoretical analysis of correlations between one`s self-esteem and sociometric status indicated inconsistency in their nature, showing them as mostly positive but also negative in some cases. It can be explained by the role sociometric status plays in a teenager with unreasonably high or low self-esteem. Teenagers with unreasonably low self-esteem tend to experience inner conflicts, thus their high sociometric status has a compensatory function. Teenagers with adequate self-esteem demonstrate a direct correlation between their self-esteem and sociometric status.
High-speed Target Tracking of 3D Object by Monocular camera

Tracking a high-speed moving target is a fundamental task in many robotic applications. There are numbers of high-speed vision systems have been developed for high-speed visual feedback [1]. In this study, we somehow focus on high-speed target tracking of both 3D position and orientation using only a monocular camera. This is a light-weight solution for many fields. We modify the pixel-wise posteriors 3D (PWP3D [2]) algorithm for high-speed target tracking and pose estimation. Experiment result show that the tracking accuracy against changes in the environment (e.g., partial occlusion) was improved compared with the original PWP3D. As a real-time application, we demonstrate our method is capable of tracking high-speed flying object.
A nonlinear version of Halanay inequality and application to neural networks theory

We establish an exponential stability result for a delayed Hopfield neural network.This is proved in case one or more of the activation functions fails to satisfy the standard Lipschitz continuity condition.We use a nonlinear version of Halanay inequality, which we prove here.
Models of Literacy Media in Improving Reading Skill of Early Grade Students

The low quality of media literacy used in reading learning causes the low reading skills of early elementary school students.Therefore, this research aims to produce a valid, practical, and effective literacy mediato improve reading skills of elementary school students.This research was a developmental researchusing 4-D development model (defining, designing, developing, and disseminating).The subjects of research were the early grade students of the elementary school in Padang Utara District, Padang City.The literacy mediawere in the form of big books, pictures media, and stories calendar.The data were obtained from media validation, teachers' and students' responses, observation, and students' learning outcomes inreading.The results showed that the students were interested in using the media for reading and feeling happy with the reading materials.It improved the students' understanding of the reading contents.Based on these results, it can be concluded that the media was valid, practical, and effective to be used to improve the reading skills of early grade elementary school students.