Reviewerly/exHarmony huggingface dataset:
MOBT: A kleptographically-secure hierarchical-deterministic wallet for multiple offline Bitcoin transactions

Small and immediate offline transactions are a trend in current bitcoin transactions. However, the previous articles mainly focused on the security of single offline transaction. In multiple offline bitcoin transactions, users deposit a small amount of bitcoins in advance into the wallet several times. For each deposit, the offline wallet has to generate a new pair of public and private keys and store it, which impose a heavy storage burden on the offline wallets and lower the efficiency of the transaction. Besides, a malicious attacker is able to embed a Secretly Embedded Trapdoor with Universal Protection (SETUP) into the offline wallet by infecting the user’s storage device, hence he can launch the kleptographic attack to get user’s private keys. In this paper, we propose a secure and efficient Bitcoin payments in multiple offline transactions. Our scheme relies on the master public key property of the hierarchical deterministic (HD) wallet to generate the key pairs of our offline wallet (MOBT). At the same time, an interactive signature protocol is used to protect MOBT from the kleptographic attack during the offline payments. We demonstrate the security of MOBT wallet and analyze the wallet’s capability to resist the kleptographic attack. The results show that the MOBT wallet is secure even if m−1 child private keys are leaked out. Furthermore, users can store their bitcoins in the wallet once. It only requires a half of the original storage volume under multiple offline bitcoin transaction. Finally, we compare the current mainstream wallets features on the market. We also discuss a novel approach for double-spending attack under offline probability payments.
Analysis of the English Language Needs of Students at the Russian Technological University

The article looks into the English language needs of engineering students and young graduates at one of the leading technological universities in Russia - NUST MISiS. The authors used a mixed methodology approach to explore how often and in what real-life situations current and former students use the English language. 107 paper questionnaires were completed by master’s degree students in class and the answers were counted; 123 responses were received online from 2015 and 2016 graduates and processed statistically; 17 structured interviews with college administrators and subject professors were recorded and then coded for the main categories. The results demonstrate that despite dramatic changes at NUST MISiS under The Competitiveness Enhancement Program 5-100, good English competence is not a priority, with the majority of young graduates being ‘unsure’ about its benefits for their career and some administrators calling it ‘the least required competence’. However, the English language needs vary across subject departments. Thus, students majoring in new materials and programming use English on a regular basis, while economists and metallurgists might not really need it in both studies and future job. Overall, the most required skill is reading, and the least used ones are oral communication and academic writing in English. The authors discuss how orientation on potential rather than actual needs can affect the ESP/EAP course design in the context of engineering education in Russia.
The Friedrichs-Lee Model and Its Singular Coupling Limit

Lee’s field-theoretical model describes the interaction between a qubit and a structured bosonic field. We study the mathematical properties of the Hamiltonian of the single-excitation sector of the theory, including a possibly “singular” qubit-field coupling (i.e., mediated by a non-square integrable form factor). This result allows for a rigorous description of qubit-field interactions in many physically interesting systems and may be extended to higher-excitation sectors of the theory.
Partially linear additive Gaussian graphical models

We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an $L_1$-regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove $\sqrt{n}$-sparsistency. Importantly, our approach avoids parametric constraints on the effects of confounders on the estimated graphical model structure. Empirically, the PLA-GGM is applied to both synthetic and real-world datasets, demonstrating superior performance compared to competing methods.
Heave compensation prediction based on echo state network with correntropy induced loss function

In this paper, a new prediction approach is proposed for ocean vessel heave compensation based on echo state network (ESN). To improve the prediction accuracy and enhance the robustness against noise and outliers, a generalized similarity measure called correntropy is introduced into ESN training, which is referred as corr-ESN. An iterative method based on half-quadratic minimization is derived to train corr-ESN. The proposed corr-ESN is used for the heave motion prediction. The experimental results verify its effectiveness.
The Impact of Home Environment on Academic Achievement of Secondary School Students

The basic objective of this research is to study effect and correlation of home environment and academic achievement of 9th class students.Sample consists of 120 students and divided into 60girls and 60 boys.9th class students chosen from school of Kota city.Random sampling method was used to collect the data.Home Environment scale, developed by A. Akhtar and S.B. Saxena ( 2011) was used to see the home environment level in each student and Academic achievement analyse through the exam results of the student.A positive relation found between home environment and academic achievement of the students and there is no significant difference in home environment of boys and girls.
The Jequié Complex Revisited: a U-Pb Geochronological Reappraisal of the Geology and Stratigraphy of the Jequié-Itagi Area (Bahia, Brazil)

New geological and litogeochemical data plus La-ICP-MS U-Pb isotopic study on the Jequié Complex, or Jequié “Block”, a granulite facies terrain of the Northeastern São Francisco Craton, allowed to propose that a new lithodemic unit, the Volta do Rio Plutonic Suite, should be created as a lower rank unit. The Jequié Complex is defined here as an intrusive complex metamorphosed in the granulite facies, containing an assemblage of plutonic calc-alkalic mafic to intermediate rocks, fractionated trondhjemites, tonalites and granodiorites, besides normal calc-alkaline tonalites/ granodiorites and rare metasedimentary remnants plus two different sets of leucogranites. The Volta do Rio Plutonic Suite is proposed as a lower range lithodemic unit belonging to the Jequié Complex. It is composed of granodiorites and monzogranites with porphyroclastic texture, even- -grained granitoids and fine-grained granitoids, besides an association of amphibole-bearing leucogranites and mafic to intermediate rocks. The metamorphosed mafic, intermediate and felsic rocks of the Jequié Complex compose a Cordilleran-type magnesian calc-alkalic association, which age is 2.7 Ga. In contrast, all the metagranitoids of the Volta do Rio Plutonic Suite show a distinctive ferroan (“A-type”) geochemical signature and the mafic and intermediate rocks associated to metaleucogranites show alkaline characteristics and host locally high-grade REE mineralizations contained in chevkinite group minerals. In the Volta do Rio Plutonic Suite, the porphyroclastic granites were dated at 2.6 Ga.The provisional age of alkaline mafic rocks with magmatic-hydrothermal REE mineralizations and of possibly coeval leucogranites is 2.5 Ga. These mineralizations are inedit in the world and the obtained time frame indicates the need to re-evaluate the geology and metallogenic potential of the Jequié Complex by considering its primordial igneous nature and by screening out its “granulitic” or “charnockitic” nature.
Lost in TLS? No More! Assisted Deployment of Secure TLS Configurations

Over the last few years, there has been an almost exponential growth of TLS popularity and usage, especially among applications that deal with sensitive data. However, even with this widespread use, TLS remains for many system administrators a complex subject. The main reason is that they do not have the time to understand all the cryptographic algorithms and features used in a TLS suite and their relative weaknesses. For these reasons, many different tools have been developed to verify TLS implementations. However, they usually analyze the TLS configuration and provide a list of possible attacks, without specifying their mitigations. In this paper, we present TLSAssistant, a fully-featured tool that combines state-of-the-art TLS analyzers with a report system that suggests appropriate mitigations and shows the full set of viable attacks.
CN-HIT-MI.T at SemEval-2019 Task 6: Offensive Language Identification Based on BiLSTM with Double Attention

Offensive language has become pervasive in social media. In Offensive Language Identification tasks, it may be difficult to predict accurately only according to the surface words. So we try to dig deeper semantic information of text. This paper presents use an attention-based two layers bidirectional longshort memory neural network (BiLSTM) for semantic feature extraction. Additionally, a residual connection mechanism is used to synthesize two different deep features, and an emoji attention mechanism is used to extract semantic information of emojis in text. We participated in three sub-tasks of SemEval 2019 Task 6 as CN-HIT-MI.T team. Our macro-averaged F1-score in sub-task A is 0.768, ranking 28/103. We got 0.638 in sub-task B, ranking 30/75. In sub-task C, we got 0.549, ranking 22/65. We also tried some other methods of not submitting results.
A new identity-based public auditing against malicious auditor in the cloud

With the development of cloud computing, the integrity of data is becoming increasingly important. The auditing schemes for data integrity allow data owners to verify the integrity of the data stored in an untrusted server. Most public auditing schemes are based on the public key infrastructure (PKI), which may lead to certificate management problems. Recently, an identity-based public auditing scheme was proposed and it could effectively reduce computation cost of auditors and solve certificate management problems. However, the scheme was proved to be insecure. In this paper, we consider the malicious auditor and propose a new identity-based public auditing against malicious auditor in the cloud computing. The new construction is proved to be secure by assuming the hardness of the computation Diffie-Hellman problem (CDHP). Finally, compared with the existing identity-based auditing scheme, our scheme is efficient and it reduces the computation overhead of the auditor.
CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations

This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext.It aimed at classifying the emotion of a user utterance in a textual conversation as happy, sad, angry or other.It is based on a large number of feature types, mainly unigrams and bigrams, which were extracted by a SAS program.The usefulness of the different feature types was evaluated by means of Monte-Carlo resampling tests.As this system does not rest on any deep learning component, which is currently considered as the state-of-the-art approach, it can be seen as a possible point of comparison for such kind of systems.
CONVOLUTIONAL NEURAL NETWORK MULTI-EMOTION CLASSIFIERS

The natural languages are universal and flexible but cannot exist without ambiguity. Having more than one attitude and meaning in the same phrase context is the main cause for word or phrase ambiguity. Most previous work on emotion analysis has only coverage single-label classification and neglect the presence of multiple emotion labels in one instance. This paper presents multi emotion classification in Twitter based on Convolutional Neural Networks (CNN). The applied features are emotion lexicons, word embeddings, and frequency distribution. The proposed networks performance is evaluated to state-of-the-art classification algorithms, achieving hamming score ranges from 0.46 to 0.52 on the challenging SemEval2018 Task E-c.
Examining a servant leadership construct and its influence on organizational citizenship behavior

This study aims to test a servant leadership construct, the Servant Leadership Survey (SLS), in the context of a collective culture. It also examines the effect of each servant leadership (SL) dimension on the organizational citizenship behavior (OCB) of employees. This study uses a validatory factor analysis method to examine the consistency of the SLS construct and, using a purposive sampling approach, involves 295 respondents working in profit and non-profit organizations. The results of this study reveal that only six dimensions of the eight-dimensional SL accord with the context of collective culture. The six dimensions are empowerment, standing back, forgiveness, courage, authenticity, and humility. The study also reports that the dimensions of standing back and authenticity have an effect on individual OCB, while the dimensions of empowerment and standing back affect organizational OCB. This study contributes to understanding different cultural contexts (individualistic versus collective) and the requirement to adjust the SL dimensions. These dimensions have different effects on OCB at the individual and organizational levels.
How does an ANT approach help us rethink the notion of site?

Although ANT has travelled far, reaching increasingly more domains and diverse non-humans, site has not yet been subject to its scrutiny. At the same time, despite its prominent status in architecture, site has received limited attention in architectural and planning theory: Reduced either to an empty container, to a silent nature ‘out there’, or to a social construction. Following the design and construction by the world-leading firm OMA of a major cultural building in Manchester, UK called Factory, in this chapter we prepare the groundwork for a different account of site. Instead of rethinking what site is, we follow the work of site-ing that includes the moves of placing and spacing simultaneously the built and the site itself, nature and culture, technologies and meaning. Tracing how site matters in design, and how architects, planners and engineers account for it, work with it and according to it, site emerges as a set of movements to follow. Not a simple resource in potentia, passively waiting the intervention of an adventurous designer in it, or a mere exercise in cultural fiction, site is rather a moving aggregate. One that travels with great speed. To trace its composition, a fast-moving ethnographer is also required, one who dares navigating through a complex ‘minefield’ of dispersed locales-of-practice, slowing down, site-ing herself and recalibrating further the ANT toolkit to be able to contribute to the growing polyphonic composition of the Factory site.
Converting a Common Low-Cost Document Scanner into a Multispectral Scanner

Forged documents and counterfeit currency can be better detected with multispectral imaging in multiple color channels instead of the usual red, green and blue. However, multispectral cameras/scanners are expensive. We propose the construction of a low cost scanner designed to capture multispectral images of documents. A standard sheet-feed scanner was modified by disconnecting its internal light source and connecting an external multispectral light source comprising of narrow band light emitting diodes (LED). A document was scanned by illuminating the scanner light guide successively with different LEDs and capturing a scan of the document. The system costs less than a hundred dollars and is portable. It can potentially be used for applications in verification of questioned documents, checks, receipts and bank notes.
Penerapan Teorema Bayes untuk Mendiagnosa Penyakit Telinga Hidung Tenggorokan (THT)

Ear, Nose and Throat Disease (ENT) is a common disease that is often considered a harmless disease by people, so they assume there is no need to see a doctor. But in fact, ENT disease can also provide serious disorders if not treated early and right, so the expert system of ENT is needed for early detection before the patient decided to see a doctor or not. Based on these problems, this study proposed the application of the Bayes theorem for early detection of ENT disease. The types of diseases used in this study were six types and had 22 symptoms. User inputs the symptoms of a disease, then the system will provide a diagnosis. This diagnosis can be used as an initial reference for sufferers and can be used as a reference for young doctors who are taking medical education with an ENT specialist. In this initial research, testing was carried out to calculate the computational time needed by the system to diagnose ENT disease. Based on this research, we found that the average computing time needed by the system to diagnose is 00:09:54 or Nine minutes, fifty-four seconds.
Contextual Guessing Technique in Reading

This research aimed to know whether students’ reading comprehension can improve with use Contextual Guessing Technique and to know how the implementation of this method in reading class. This research used action research classroom. The finding of the research was the students more enthusiast in answering the reading’s question. The test result came from first cycle and second cycle. It showed improvement. The conclusion was the students need learning technique relate with their learning level. Contextual Guessing Technique is one of the appropriate techniques in reading class for senior high students. This case proved by the students more motivated and spirit in learning English especially in reading. Therefore the students got good score.
Expert RF Feature Extraction to Win the Army RCO AI Signal Classification Challenge

Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. Feature generation, where raw signal information is transformed prior to attempting classification is a key part of this process. A big question that researchers face is whether to let the deep learning system infer the relevant features or build expert features based on expected signal characteristics. In this paper, we present novel signal feature extraction methods for use in signal classification via ML. The deep learning and combined approaches are discussed in a simultaneous publication. Expert features were utilized via ensemble leaning and shallow neural networks to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. The features include both standard statistical measurements such as variance and kurtosis, as well as measurements tailored for specific waveform families. We discuss the best statistical descriptors along with a ranked list of signal features and discuss individual feature importance. We then demonstrate our implementation of these features and discuss effectiveness in estimating different modulation classes. The methods discussed when combined with deep learning are capable of correctly classifying waveforms at -10 dB SNR with over 63\% accuracy and signals at +10 dB SNR with over 95\% accuracy from an Army RCO provided training set.
Improving IoT Predictions through the Identification of Graphical Features

IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features that are to be used by a classifier for prediction tasks. We show that a set of generic graph-based features can improve performance of sensor network predictions without the need for application-specific and task-specific feature engineering. We apply this approach to three different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. Our approach produced comparable results with most of the state-of-the-art methods, while maintaining the additional advantage of general applicability to IoT sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. We further investigate the impact of using edge-transition times, categorical features, different sensor window sizes, and normalization in the smart home domain. We also consider deep learning approaches, including the Graph Convolutional Network (GCN), for the elimination of feature engineering in the smart home domain, but our approach provided better performance in most cases. We conclude that the graphical feature-based framework that is based on IoT sensor categorization, nodes and edges as features, and feature selection techniques provides superior results when compared to the non-graph-based features.
Polynomial expressions of <i>p</i>-ary auction functions

Abstract One of the common ways to design secure multi-party computation is twofold: to realize secure fundamental operations and to decompose a target function to be securely computed into them. In the setting of fully homomorphic encryption, as well as some kinds of secret sharing, the fundamental operations are additions and multiplications in the base field such as the field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mi>𝔽</m:mi> <m:mn>2</m:mn> </m:msub> </m:math> {\mathbb{F}_{2}} with two elements. Then the second decomposition part, which we study in this paper, is (in theory) equivalent to expressing the target function as a polynomial. It is known that any function over the finite prime field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mi>𝔽</m:mi> <m:mi>p</m:mi> </m:msub> </m:math> {\mathbb{F}_{p}} has a unique polynomial expression of degree at most <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>p</m:mi> <m:mo>-</m:mo> <m:mn>1</m:mn> </m:mrow> </m:math> {p-1} with respect to each input variable; however, there has been little study done concerning such minimal-degree polynomial expressions for practical functions. This paper aims at triggering intensive studies on this subject, by focusing on polynomial expressions of some auction-related functions such as the maximum/minimum and the index of the maximum/minimum value among input values.
Sentiment Analysis through Transfer Learning for Turkish Language

Sentiment Analysis (SA) has received much attention in recent years. In this paper, we proposed a model based on the transfer learning technique to address SA problem. First, we utilize word embeddings that are trained on 322K documents from Turkish Wikipedia. The model employs a regular Long Short-Term Memory (LSTM) with dropout. Secondly, we fine-tuned the pre-trained language model on two different target datasets (restaurant and product reviews) independently. Finally, the LSTM is trained to classify reviews according to positive and negative sentiments and its associated performance is assessed. This study is also considered to be the important attempt that uses transfer learning by applying a fine-tuning technique and deep learning architecture to address SA problem for Turkish Language.