Reviewerly/exHarmony huggingface dataset:
Text Adaptation for Speaker Verification with Speaker-Text Factorized Embeddings

Text mismatch between pre-collected data, either training data or enrollment data, and the actual test data can significantly hurt text-dependent speaker verification (SV) system performance. Although this problem can be solved by carefully collecting data with the target speech content, such data collection could be costly and inflexible. In this paper, we propose a novel text adaptation framework to address the text mismatch issue. Here, a speaker-text factorization network is proposed to factorize the input speech into speaker embeddings and text embeddings and then integrate them into a single representation in the later stage. Given a small amount of speaker-independent adaptation utterances, text embeddings of target speech content can be extracted and used to adapt the text-independent speaker embeddings to text-customized speaker embeddings. Experiments on RSR2015 show that text adaptation can significantly improve the performance of text mismatch conditions.
Transys: Leveraging Common Security Properties Across Hardware Designs

This paper presents Transys, a tool for translating security critical properties written for one hardware design to analogous properties suitable for a second design. Transys works in three passes adjusting the variable names, arithmetic expressions, logical preconditions, and timing constraints of the original property to retain the intended semantics of the property while making it valid for the second design. We evaluate Transys by translating 27 assertions written in a temporal logic and 9 properties written for use with gate level information flow tracking across 38 AES designs, 3 RSA designs, and 5 RISC processor designs. Transys successfully translates 96% of the properties. Among these, the translation of 23 (64%) of the properties achieved a semantic equivalence rate of above 60%. The average translation time per property is about 70 seconds.
Improving Neural Machine Translation with Linear Interpolation of a Short-Path Unit

In neural machine translation (NMT), the source and target words are at the two ends of a large deep neural network, normally mediated by a series of non-linear activations. The problem with such consequent non-linear activations is that they significantly decrease the magnitude of the gradient in a deep neural network, and thus gradually loosen the interaction between source words and their translations. As a result, a source word may be incorrectly translated into a target word out of its translational equivalents. In this article, we propose short-path units (SPUs) to strengthen the association of source and target words by allowing information flow over adjacent layers effectively via linear interpolation. In particular, we enrich three critical NMT components with SPUs: (1) an enriched encoding model with SPU, which interpolates source word embeddings linearly into source annotations; (2) an enriched decoding model with SPU, which enables the source context linearly flow to target-side hidden states; and (3) an enriched output model with SPU, which further allows linear interpolation of target-side hidden states into output states. Experimentation on Chinese-to-English, English-to-German, and low-resource Tibetan-to-Chinese translation tasks demonstrates that the linear interpolation of SPUs significantly improves the overall translation quality by 1.88, 1.43, and 3.75 BLEU, respectively. Moreover, detailed analysis shows that our approaches much strengthen the association of source and target words. From the preceding, we can see that our proposed model is effective both in rich- and low-resource scenarios.
HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter

In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC's disadvantages. One model contained the target's prior color information, and the other the target's prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram-Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker's performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.
Gaussian Pseudorandom Number Generator Based on Cyclic Rotations of Linear Feedback Shift Registers

This paper presents a new proposal to generate optimal pseudorandom numbers with Gaussian distribution. The generator is especially designed for low-cost hardware implementation, although the software version is also considered. For this reason, Linear Feedback Shift Registers in conjunction with cyclic rotations are employed. The proposal presents a low implementation cost and overcomes the limitations of the previous Gaussian generators based on linear feedback shift registers by means of a less complex algorithm to find optimal configurations. As a consequence, it turns into a really usable generator. Moreover, a further improvement, based on the simulated annealing algorithm, is applied in order for the random values to be better adjusted to the normal distribution.
About the foundation of the Kubo generalized cumulants theory: a revisited and corrected approach

Abstract More than fifty years ago, in a couple of seminal works (Kubo 1962 J. Phys. Soc. Japan 17 1100–20; Kubo 1963 J. Math. Phys . 4 174–83) Kubo introduced the important idea of generalized cumulants, extending to stochastic operators this concept implicitly introduced by Laplace in 1810. Kubo’s idea has been applied in several branches of physics, where the result of the average process is a Lioville operator or an effective time evolution operator for the density matrix of spin systems or the reduced density matrix for boson-fermions etc. Despite this success, the theoretical developments in these Kubo works pose problems that were highlighted many years ago by Fox and van Kampen and never solved. These weaknesses and errors, in particular concerning the factorization property of exponentials of cumulants and the explicit expressions that give generalized cumulants in terms of generalized moments and vice-versa, caused some perplexity (and confusion) about the possible application of this procedure, limiting its use, in practice. In the present paper, we give a sound ground to the approach to cumulant operators, working in a general framework that shows the potentiality of the old Kubo idea, today not yet fully exploited. It results that, for the same moment operators, different definitions of generalized cumulants can be adopted. A general Kubo–Meeron closed-form formula giving cumulant operators in terms of moment operators cannot be obtained, but the reverse one, cumulants in terms of operators, is given and, noticeably, formally it does not depend on the specific nature of the moments, but just on the definition of the generalized cumulants.
THE ROLE OF ELECTRONIC PEDAGOGICAL TOOLS IN HIGHER EDUCATION

The article gives the interpretation of the concept of the role of computer technology in higher education, considers the use of computer technology in educational institutions. However, the role of e-learning tools in higher pedagogical education, which provide universal computerization of students and teachers at a level that allows solving at least three main tasks: providing Internet access for each participant in the educational process, and, preferably, at any time and from various places of stay; development of a single information space of educational industries and the presence in it at different times and independently of all other participants in the educational and creative process; the creation, development and effective use of managed educational information resources.
JMNet: A joint matting network for automatic human matting

Abstract We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images. We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module, which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques; the shared encoder leads to better performance and lower memory costs. Our model can process real images downloaded from the Internet for use in composition applications.
PredatorHP Revamped (Not Only) for Interval-Sized Memory Regions and Memory Reallocation (Competition Contribution)

Abstract This paper concentrates on improvements of the PredatorHP shape analyzer in the past two years, including, e.g., improved handling of interval-sized memory regions or new support of memory reallocation. The paper characterizes PredatorHP’s participation in SV-COMP 2020, pointing out its strengths and weakness and the way they were influenced by the latest changes in the tool.
Research on a high‐order AES mask anti‐power attack

The cryptographic algorithm has been gradually improved in design, but its implementations are vulnerable to side-channel analysis (SCA). Generally speaking, adding a mask to the primitive is the best way to counteract SCA. In the high-order mask, the key to affecting performance and security lies in the multiplication design. Based on the research of the advanced encryption standard (AES) algorithm, internal round function structure, and zero-knowledge proof, a high-order AES mask scheme is designed to optimise the implementation. In this scheme, the substitution-box protects sensitive variables in the algorithm with the use of secure multiplication and secure inversion by column. The scheme named as in columns higher-order mask (ICHM), features low cost and high security. The result of the experiment proves the security and effectiveness of the ICHM.
Correspondence Matching and Time Delay Estimation for Hand-eye Calibration

For visually aided industrial robots, the transformation between the mounted visual information system and the end-effector must be calibrated prior to the practical use. However, during the data acquisition stage, measurement uncertainties will lead to inevitable mismatched data. This article proposes a novel correspondence matching method for the hand-eye calibration system of the type AX = X B. The correspondence matching refers to the problem of finding out the well-matched A and B and the relative time delay between them, from a series of measurements. A neat Lie algebra formulation has been obtained to cast the original problem into a high-dimensional point-cloud registration problem. This solution allows for the simultaneous solution of the hand-eye calibration, correspondence matching, and time-delay estimation as well as the uncertainty description of the obtained results. Synthetic simulations verify the correctness of the proposed method. Experimental studies have also confirmed its practical feasibility on an automatic welding platform.
High-dynamic-range areal profilometry using an imaging, dispersion-encoded low-coherence interferometer

This work presents the design and characterization of an approach for areal surface profilometry with sub-nm axial resolution. The developed approach is based on a low-coherence interferometer enhanced by an dispersive element to control the axial resolution and measurement range. Optical path differences are detected by an imaging spectrometer where equalization wavelengths are determined as a basis for fitting spectra. This enables the acquisition of surface profiles with a length of up to 1.5 mm without mechanical scanning where a minimal resolution of 0.1 nm in an axial measurement range of nearly 80 µm was achieved. The resolution calculation was based on the standard deviation of measured feature heights. In addition to the system design, its capabilities are demonstrated on samples such as height standards.
OCR for Printed Bangla Characters Using Neural Network

Optical Character recognition is a buzzword in the field of computing.Artificial neural networks are being used to recognize characters for a long time.ANN has the ability to learn and model non-linear and complex relationships, which is really important because in real life, many of the relationships between inputs and outputs are non-linear as well as complex.Research in the field of OCR with Bangla language is not as vast as the English language.So, there is a scope of research in this area.It can be used to search and scan hundreds of Bangla documents within seconds and can easily manipulate the data.It is developed for various purpose like for vision impaired person where OCR software can help turn books, magazines and other printed documents into accessible files that they can listen.The limitation of traditional OCR are sufficient dataset is not available, all different font of characters are not available and there are lots of complex and similar shape characters for which accuracy not good.In our research, we first tried to make a dataset large enough so that we can train our neural network as they require big data to train.We built our own dataset of 2,97,898 Bangla single character images of different fonts .Then for implementing neural network we used Scikit-learn's multi-layer perceptron classifier and we also implemented our own multi-layer feed forward back propagation neural network using a machine learning framework named Tensorflow.We have also built a GUI application to demonstrate the recognition of Bangla single character images.
Secure multiparty quantum computation with few qubits

We consider the task of secure multiparty distributed quantum computation on a quantum network. We propose a protocol based on quantum error correction which reduces the number of necessary qubits. That is, each of the $n$ nodes in our protocol requires an operational workspace of ${n}^{2}+4n$ qubits, as opposed to the previously shown $\mathrm{\ensuremath{\Omega}}\left(({n}^{3}+{n}^{2}{s}^{2})logn\right)$ qubits, where $s$ is a security parameter. Additionally, we reduce the communication complexity by a factor of $O({n}^{3}log(n))$ qubits per node compared to existing protocols. To achieve universal computation, we develop a distributed procedure for verifying magic states, which allows us to apply distributed gate teleportation and which may be of independent interest. We showcase our protocol in a small example for a seven-node network.
Guided Interactive Learning through Chatbot using Bi-directional Encoder Representations from Transformers (BERT)

In this digital era, the smart application is ought to continuously generate a huge amount of data into existence. These data can be utilized to gain a large amount of information that deploys numerous uses. Education is one of the key fields that generate huge amounts of data in existence. However, it is difficult to obtain only the required information because of the speed and volume of data being generated from numerous online educational resources. One of the tools that can be useful in extracting useful information from textual data is text summarization and analysis tools. Many text summarization tools are being developed but largely focus on summarizing a single document effectively. This project aims to create a text summarization tool using natural language processing techniques that can extract relevant and important information from multiple documents to enable users to learn effectively. This information can be presented to users interactively and effectively through a chatbot interface. The tool also performs multiple analyses on the user responses provided to the chatbot to control the conversational flow and personalize the user experience to enhance learning.
Analisis Forensik Smartphone Android Menggunakan Metode NIST dan Tool MOBILedit Forensic Express

Technological advances are growing rapidly, including mobile device technology, one of which is an Android smartphone that is experiencing rapid progress with a variety of features so that it can spoil its users, with the rapid development of smartphone technology, many users benefit, but many are disadvantaged by the growing smartphone. technology, so that many perpetrators or persons who commit crimes and seek profits with smartphone facilities. Case simulation by securing Samsung Galaxy A8 brand android smartphone evidence using the MOBILedit forensic express forensic tool with the National Institute of Standards and Technology (NIST) method which consists of four stages of collection, examination, analysis and reporting. The results of testing the Samsung Galaxy A8 android smartphone are carried out with the NIST method and the MOBILedit Forensic Express tool obtained by data backup, extraction and analysis so that there are findings sought for investigation and evidence of crimes committed by persons using android smartphone facilities.
Exploring the Success of GMT Technique: Games, Mind-Mapping, and Twitter Hashtags in Teaching Vocabulary in EFL Higher Education Environment

Vocabulary is an essential element of language learning. Wide ranges of vocabulary along with grammatical competence guarantee learners to communicate in the language effectively. This study proposes an edutainment method for learning vocabulary by simply combining education and entertainment. This study aims to gain insights about learners’ opinions and perspectives about the use of a technique developed by the researchers as well as how participants feel about their learning. The study investigates the effect of employing Games, Mind-mapping and Twitter Hashtags as the GMT technique, on female Saudi university students’ achievement in English vocabulary. The study suggests that this technique which consists of interactive games, cognitive mind-mapping and the exploitation of technology in the form of twitter hashtags, all employed together, constitute a unified framework for activating students’ vocabulary learning. The sample in the study consisted of 150 students enrolled in the vocabulary building course during the second semester of the academic year 2018/2019. The participants were asked to respond to the questionnaire and they also took variant assessment tests, then their scores were compared to the results of other students who were not taught vocabulary using the technique in question. The findings ascertain the improvement and significant in the experimental group. In addition, the results reveal that the learners had mostly positive opinions on implementing the GMT technique which facilitated their language learning experience. The researchers conclude that the GMT technique can be an effective tool to promote students’ active engagement, motivation, and interaction in vocabulary learning.
A contextual conditional random field network for monocular depth estimation

Monocular depth estimation plays a crucial role in understanding 3D scene geometry and is a challenging computer vision task. Recently, deep convolutional neural networks have been applied to solve this problem. However, existing methods either directly exploiting RGB pixels which can introduce much noise into the depth map or utilizing over smoothed internal representation features which can cause blur in the depth map. In this paper, we propose a contextual CRF network (CCN) to tackle these issues. The new CCN adopts the popular encoder-decoder architecture with a new contextual CRF module (CCM) which is guided by the depth features and regularizes the information flow from the encoder layer to the corresponding layer in the decoder, thus can reduce the mismatch between the RGB pixel and the depth map cue while at the same time retain detail features to output a fine-grained depth map. Moreover, we propose a depth-guided loss function which pays a balanced attention to near and far pixels thus addressing the long-tailed distribution of depth information. We have conducted extensive experiments on three public datasets for monocular depth estimation. Results demonstrate that our proposed CCN achieves superior performances in terms of visual quality and competitive quantitative results when compared with state-of-the-art methods.
An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection

An enhanced multimodal stacking scheme is proposed for quick and accurate online detection of harmful pornographic contents on the Internet. To accurately detect harmful contents, the implicative visual features (auditory features) are extracted using a bi-directional RNN (recurrent neural network) with VGG-16 (a multilayered dilated convolutional network) to implicitly express the signal change patterns over time within each input. Using only the implicative visual and auditory features, a video classifier and an audio classifier are trained, respectively. By using both features together, one fusion classifier is also trained. Then, these three component classifiers are stacked in the enhanced ensemble scheme to reduce the false negative errors in a serial order of the fusion classifier, video classifier, and audio classifier for a quick online detection. The proposed multimodal stacking scheme yields an improved true positive rate of 95.40% and a false negative rate of 4.60%, which are superior values to previous studies. In addition, the proposed stacking scheme can accurately detect harmful contents up to 74.58% and an average rate of 62.16% faster than the previous stacking scheme. Therefore, the proposed enhanced multimodal stacking scheme can be used to quickly and accurately filter out harmful contents in the online environments.
E-government learning media through augmented reality technology

&#x0D; &#x0D; &#x0D; &#x0D; Augmented reality (AR) is one of the newest learning technologies besides Artificial Intelligent, Virtual Reality, Virtual World, Alternates Reality Games, and Wearable Computing. E-Government is becoming an evolving academic field and transforming to be E-Governance, E-Participation, E-Service, E-Democracy, E-Voting issues. Therefore, some universities have E-Government course in their curriculum. AR can be one of the learning media to learn and teach E-Government effectively and interestingly. This research aims and contributes to develop a novel model of E-Government learning media using AR. Furthermore, this work has three novelties as follows:First, a method to develop e-Government learning media using AR. Second, a novel model of e-government learning media based on augmented reality technology. Third, the material courses are command centre and public service mall materials based in Surabaya, Indonesia. The new model of learning media using AR consists of main components, i.e., user, devices, AR, and course materials. Additionally, this novel model has some sub-components, such as AR object, marker, text, image, audio, video, and 3D animation. The proposed model has some practical and theoretical implications. For the theoretical implication , it adds a model of learning media for e-government course and extends the body of knowledge for e-government and augmented reality fields. For practice, e-Government courses can uilize the augmented reality application.&#x0D; &#x0D; &#x0D; &#x0D;