Reviewerly/exHarmony huggingface dataset:
Random flip-flop: adding quantum randomness to digital circuits for improved cyber security, artificial intelligence and more

Harnessing quantum randomness for the generation of random numbers is an important concept crucial for information security and many other computer-related applications. Quantum random number generators (QRNGs) are evolving from bulky, slow, and expensive implementations towards chip-sized devices. Since computers are deterministic devices, and as such incapable of generating randomness, the prevailing modus operandi is that a QRNG is added to a computer, realizing what is known as "probabilistic Turing machine". State-of-the-art QRNGs use different hardware ports (USB, PCIe,...) and manufacturer-specific bit-transfer protocols, which limits their use by both hardware and software developers. In this work we propose an entirely different approach to use of randomness in a general digital environment (computers included), via a new, dedicated logic circuit: the random flip-flop (RFF), which is fully inter-operable with the standard logic circuits and can form an integral part of microprocessors, ultimately as an instruction-set extension. RFFs can be regarded as the missing link that closes the full set of logic elements; we show their use in digital and analog applications. We also build an RFF on a silicon chip using CMOS process. That, in principle, allows having any number of RFFs on a particular chip (e.g. microprocessor, FPGA, ASIC, ...), enabling both a very fast and massively parallel random number generation. Applications are endless, including recent trends in artificial intelligence, biomimetic stochastic computer and graphics processors. Integrated RFF does not need hardware ports nor bit-transfer protocols and allows an easy integration of randomness into higher programming languages.
NLM at BioASQ Synergy 2021: Deep learning-based methods for biomedical semantic question answering about COVID-19

The COVID-19 outbreak has heightened the need for systems that enable information seekers to search vast corpora of scientific articles to find answers to their natural language questions. This paper describes the participation of the U.S. National Library of Medicine (NLM) team in BioASQ Task Synergy on biomedical semantic question answering for COVID-19. In this work, we exploited the pre-trained Transformer models such as T5 and BART for document re-ranking, passage retrieval, and answer generation. Official results show that among the participating systems, our models achieve strong performance in document retrieval, passage retrieval, and the “ideal answer” generation task. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Shortest Vectors in Lattices of Bai-Galbraith’s Embedding Attack on the LWR Problem

The Learning With Rounding (LWR) problem has attracted increasing attention as a foundation for post-quantum cryptosystems. It is known to be a variant of the Learning With Errors (LWE) problem, and so far the computational hardness of the LWE problem has been analyzed through various types of attacks using the structure of lattices. Bai-Galbraith’s embedding attack is one of the most effective attacks against the LWE problem. Their embedding attack is also applicable to the LWR problem - through the transformation from the LWR problem to the LWE problem - and its effect on the LWR problem has been directly analyzed with the structure of a certain lattice (referred to as a BG-lattice in this paper) constructed in the LWE problem. However, the structure of a BG-lattice in the LWR problem is not the same as that in the LWE problem with this transformation; thus, it requires more concrete investigation for the security analysis of LWR-based cryptosystems. In this paper, we study the structure of a BG-lattice constructed in the LWR problem through the transformation from the LWR problem to the LWE problem. Specifically, we explicitly find a certain vector in the lattice that can be the shortest, and formulate the condition where such a vector is surely the shortest one. The existence of such a shortest vector causes a situation that the second shortest vector linearly independent of the shortest vector in a BG-lattice is different from the expected. We also study the probability that this situation occurs, and obtain a relation between the probability and parameters of the LWR problem. Our experimental results confirm the existence of this shortest vector and the aforementioned relation. Note that the focus of this paper is a theoretical analysis, and applying it to the security analysis of LWR-based cryptosystems will be conducted in future work.
Research of Modified Hyper-beam Performance based on Passive Sonar

The key to array signal processing is to enhance the processing gain in the background of strong reverberation and high noise while the processing gain plays an important role in improving system detection performance and reducing target detection false alarms. Traditional weighted processing method mainly reduces the side lobe and sharp the beam-width, however, it is difficult to reconcile the two aspects. The output of the hyper-beam loses phase information, and thus post-processing such as spectrum analysis cannot be performed. This paper introduces an idea of modified hyper beam, and then apply it to space-time processing of passive signals. The modified hyper-beam technique uses the output of hyper-beam as weight coefficient to the conventional beam output, which can retain the beam output phase information while reducing the main lobe width and side lobes. The experiment data analysis based on passive sonar verifies that the processing gain brought by the method in this paper can further improve the detection performance and improve the tracking effect.
Confidentiality of Data using Hash Function

Steganography and Cryptography are two major fields which is widely used for data security. With the help of these technologies, data security is provided in banking system. In the proposed method, the online transactions are done virtually using hash function. Encryption of data is done by using the cryptographic hash function algorithm. The proposed approach implements an efficient algorithm for embedding the data in an image using steganography which provides the better security pattern for sending messages through a network. The authentication details of the sender and the receiver is hidden to achieve a secure transmission. The proposed approach provides better integrity and confidentiality. This paper implemented a novel methodology which can be used as a secure and highly efficient method of data hiding and data extracting.
A Preliminary Study on the Segmentation Algorithm of Dongba Scripture

Dongba script is the only surviving hieroglyph in the world, and is known as the “living fossil” of writing. In 2003, the Dongba Scripture written in Dongba hieroglyph was included in the memory of the World Heritage List by UNESCO. Therefore, we realized an automatic segmentation algorithm for Dongba Scripture documents in sentence units. It improves the recursive polytomy algorithm based on projection. The algorithm is simple to implement, and is proved by experiments that it has high accuracy and robustness. And it provides more information for studying the layout rules, contextual connections, and the layout of Dongba Scriptures at a smaller level.
Text Detection in Tibetan Ancient Books: A Benchmark

The digitization of Tibetan ancient books is of great significance to the preservation of Tibetan culture. This problem involves two tasks: Tibetan text detection and Tibetan text recognition. The former is undoubtedly crucial to automatic Tibetan text recognition. However, there are few works on Tibetan text detection, and lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper, we introduce the TxTAB dataset for evaluating text detection methods in Tibetan ancient books. The dataset is established based upon 202 treasured handwritten ancient Tibetan text images and is densely annotated with a multi-point annotation method without limiting the number of points. This is a challenging dataset with good diversity. It contains blurred images, gray and color images, the text of different sizes, the text of different handwriting styles, etc. An extensive experimental evaluation of 3 state-of-the-art text detection algorithms on TxTAB is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements particularly for detecting Tibetan text in images of low quality.
Analysing the Adaptability of Software Defect Prediction in Small Software Firms

Software Defect Prediction [SDP] assumes an imperative job in the dynamic research territories of software engineering. A software defect is an error, bug, glitch or error in software that makes it make a wrong or startling result. The significant hazard factors related with a software defect which isn't recognized amid the early period of software development are time, quality, cost, exertion and wastage of assets. Defects may happen in any period of software development. Blasting software organizations center fixation on the software quality, especially amid the early period of the software development. Thus the key goal of any association is to decide and address the defects in an early period of development process of the software. The research carried out in this paper shows how we have attempted to perceive the troubles in Software Defect Prediction with the help of a survey outline, a sorted out survey toward this way was set up with 71 questions and 370 respondents down the results using IBM SPSS Tool.
Accelerating $t$-SNE using Fast Fourier Transforms and the Particle-Mesh Algorithm from Physics

<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -Distributed Stochastic Neighbor Embedding ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE) is a well-known dimensionality reduction technique used for the visualization of high-dimensional data. However, despite several improvements, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE is not well-suited to handle large datasets. Indeed, for large datasets, the computation time required to obtain the visualizations is still too high to incorporate it in an interactive data exploration process. Since <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE can be seen as an N -body problem in physics, we present a new variant of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE based on a popular algorithm used to solve the N -body problem in physics called Particle-Mesh (PM). The problem is solved by first computing a potential in space and deriving from it the force exerted on each body. As the potential can be computed efficiently using Fast Fourier Transforms (FFTs), this leads to a significant speed up. The mathematical correspondence between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -SNE and PM presented in this work could also lead to other future improvements since more advanced PM algorithms have been developed in physics for decades.
Studying the Influence of Empathy Maps on Brainstorming for Requirements Elicitation: A Quasi-Experiment

There is currently a trend that highlights the interest in the use of Design Thinking techniques for the elicitation of requirements. The evidence concerning the effectiveness of these techniques is, however, still scarce, which has consequently led us to carry out a quasi-experiment in order to evaluate the effectiveness of one of the most frequently used DT techniques: Empathy Maps. Empathy Maps were employed by students enrolled on a Bachelor’s degree in Computer to understand their influence on the effectiveness of Brainstorming sessions. The students perceived a slightly positive influence as regards the use of the Empathy Maps as part of a requirements elicitation, despite not finding statistically positive results. We obtained several insights that may be useful for practitioners, researchers and lecturers interested in using this type of techniques to improve the requirements elicitation practices, although this preliminary result is not conclusive and should be corroborated in further studies.
Improving Online Product Marketing Learning Achievement in Creative Products and Entrepreneurship Subjects for Class XII Accounting Students Using Problem Based Learning Learning Models at SMKN 1 Bunguran Barat

This research was carried out at SMK N 1 Bunguran Barat in Class XII Accounting, where the ability of students for online product marketing material in the eyes of each product course was low. The purpose of this classroom action research research is to find out whether the Problem Based Learning method can improve student learning achievement. The data collection method is daily practice exercises followed by a learning achievement test. The data analysis method is descriptive. The results obtained from this study are the group assignment method with the Problem Based Learning Model which can improve the learning achievement of students. This is evident from the results obtained initially reaching an average value of 48.75, in the first cycle the average value was 67.50 and in the second cycle the average value was 73.88. The conclusion obtained from this study is that the Problem Based Learning model through independent and group assignment techniques can improve learning achievement
An Investigation on the Use of WhatsApp Groups as a Mobile Learning System to Improve Undergraduate Performance

This study investigates the use of WhatsApp as a Mobile Learning System on freshman and sophomore students doing multimedia-based courses. The study uses a conceptual model based on the second thesis of Anderson’s Interactivity Equivalency Equation to determine the impact the use of the technology has on student learning outcomes. Results showed that the most noticeable factor of interaction was the student to teacher construct. Due to their interaction on the WhatsApp group with their peers, content, and teacher, they achieved higher final results. Significant factors were the value placed on real-time chatting with peers, practical assessment on overall learning, and the reading of instructor posts. 85% of the control group had GPA of 2.57 and below, which suggests that the lack of the additional interaction may have been a contributing factor to lower GPAs than the experimental group which had a mean GPA of 2.85, with the highest recorded ones being 3.71.
Gait control method of multifunctional outdoor intelligent mobile robot based on parameter identification

In order to effectively improve the gait control accuracy and control efficiency of the multifunctional outdoor intelligent mobile robot, a multifunctional outdoor intelligent mobile robot gait control method based on parameter identification is proposed. Based on the construction of the gait stability model of the outdoor intelligent mobile robot, a three-layer motion control system of the outdoor intelligent mobile robot integrating time and position control and motion control is designed. Combining the principle of parameter identification, the robot moves the gait virtual constraint. Adjust the posture characteristics of the robot gait control layer, establish the relationship between posture and structure, adopt distributed priority experience playback technology, establish an efficient robot gait control method, and realize intelligent mobile robot gait control. The simulation results show that the proposed control method can enable the multifunctional outdoor intelligent mobile robot to overcome the interference of uneven terrain, effectively control the gait changes, and improve the efficiency of gait control.
Expertise Detection in Crowdsourcing Forums Using the Composition of Latent Topics and Joint Syntactic–Semantic Cues

Abstract We develop an NLP method for inferring potential contributors among multitude of users within crowdsourcing forums (CSFs). The method basically provides a way to predict expertise from their structures (syntax–semantic patterns) when crowdsourced votes are unavailable. It primarily deals with tackling core adverse conditions, which hinder the identification of crowds’ expertise levels, and standardization of measuring linguistic quality of crowdsourced text. To solve the former, an expertise estimation and linguistic feature annotation algorithm is developed. To approach the later, a comprehensive linguistic characterization of crowdsourced text, along with extensive joint syntax–punctuation analyses, have been carried out. The entire corpora are comprised of approximately 8 different domains, 3 million and 50,000 sentences, and 32 million and 90,000 words, contributed by a crowd of 50,000 users. The analyses revealed six major linguistic patterns, identified on the basis of ordered lists of structural (syntactic) categories, learned from grammatical constructions, practiced by major groups of experts. In addition, nine different text-oriented expertise dimensions are identified, as crucial steps towards establishing standard linguistic-based expertise-framework for most CSFs. Potentially, the resulting framework simplifies the measurement of crowds’ proficiency, in those particular forums, where crowds’ tasks (e.g., answering questions, technically discerning deep features within images of galaxies for classifying them into certain categories) are intimately connected with their writing (e.g., describing answers illustratively, expressing complex phenomena observed in classified images). Moreover, wide varieties of linguistic annotations: latent topic annotations, named entities, syntactic and punctuation annotations, semantic and character set annotations, word and character n-grams ( n = 2 and 3) annotations, are extracted. That is for building baseline and enhanced versions of expertise models (about 20 different models built). The successive achievements of enhancing baseline models, with iteratively adding linguistic feature annotations in a two-stage enhancement process, indicate the adaptability of the learned models.
Not Only the Contextual Semantic Information: A Deep Fusion Sentimental Analysis Model Towards Extremely Short Comments

Extremely short comments (ESC) often contain rich information to convey users’ emotions towards content. However, conducting sentiment analysis on ESC is challenging due to the limited contextual semantic information and colloquial expressions. Traditional methods mainly focus on contextual text features. In this work, we propose a novel model, named Chinese Phonetic-Attentive Deep Fusion Network (CPADFN) that attentively fuse the Chinese phonetic alphabet features of the ESC, meta-information about the ESC along with the contextual text features. First, the multi-head self-attention mechanism is utilized to obtain the phonetic alphabet representation and the sentence representation separately. Also, a fully-connected layer is used on the embeddings of the meta-information about the ESC to obtain the meta-information representation. Then, the local activation unit is employed to attentively fuse these feature representations. Bi-LSTM is applied to address the sequence dependency across these fused features separately. Third, a fully-connected layer with softmax function is applied to predict emotional labels. We conduct experiments on a self-crawled ESC dataset DanmuCorpus, and two public Chinese short text datasets, MovieReview and WeiboCorpus. The experimental results demonstrate that CPADFN achieves better performances.
Use Case: Information Security Risk Assessment for Providers of Services in a Virtual Environment

Information and data in today’s world are the most valuable assets of an organization. Every business system, and thus the information and data that are part of such a system, is exposed to certain risks and threats. For this reason, organizations are forced to protect their assets. One of the key parameters that affect an organization’s exposure to risk is information security. Its goal is to protect information from threats. The information security risk management system establishes a mechanism for controlling and managing an acceptable level of risk in the organization. Selecting an appropriate risk assessment methodology allows the organization’s managers to prioritize risks according to their severity or some other criteria. Given the complexity of today’s information business, the Probabilistic Risk Assessment methodology has begun to be applied in information security risk assessment. By creating a use-case scenario, it is possible to conduct qualitative and quantitative risk assessments. Event trees and stable errors as part of the above methodology are used to indicate possible scenarios, as well as to find the causes of their occurrence and to model the possible failure of controls applicable to risk mitigation. This paper will present one of the use cases of information security risk assessment using these trees. Also, their application in the conditions of a complex information system will be presented on the example of an organization in the Republic of Croatia.
Free Speech, Privacy, and Autonomy

While autonomy arguments provide a compelling foundation for free speech, they also support individual privacy rights. Considering how speech and privacy may be justified, I will argue that the speech necessary for self-government does not need to include details that would violate privacy rights. Additionally, I will argue that if viewed as a kind of intangible property right, informational privacy should limit speech and expression in a range of cases. In a world where we have an overabundance of content to consume, much of which could be called “information pollution,” and where there are numerous platforms to broadcast one’s expressions, it is increasingly difficult to maintain that speech should trump privacy.
Research on attention and reasoning mechanism of autonomous English learning based on machine learning

Abstract The wave of the Internet era has swept over our lives and learning, and the Internet is gradually being widely used in all scenes of life and learning. The use of Internet technology can greatly improve efficiency compared to traditional methods. With the popularization of computers, internet technology has entered millions of households, and now more and more school courses are related to the Internet. However, the teaching evaluation of online learning is generally derived from the post-class feedback of teachers and students. In this paper, we analyze the video feeds of students during class and then qualitatively assess students' attention. In this paper, the English grammar error correction model is mainly based on deep learning, and the English grammar error correction problem is defined. The English grammar error correction evaluation criteria are given. Besides, the SEQ-based English grammar error correction model is proposed, and some improvements are made to the basic SEQ model, and then the algorithmic model is experimented and validated. Based on the above discussion, the feedback mechanism is proposed. Moreover, we also introduce the N-gram model and related implementation techniques, and experiments are conducted and experimental results are given with the corpus.