Reviewerly/exHarmony huggingface dataset:
Consecutive Decoding for Speech-to-text Translation

Speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT), an integral approach for speech-to-text translation. The key idea is to generate source transcript and target translation text with a single decoder. It benefits the model training so that additional large parallel text corpus can be fully exploited to enhance the speech translation training. Our method is verified on three mainstream datasets, including Augmented LibriSpeech English-French dataset, TED English-German dataset, and TED English-Chinese dataset. Experiments show that our proposed COSTT outperforms the previous state-of-the-art methods. The code is available at https://github.com/dqqcasia/st.
Image Inpainting With Learnable Edge-Attention Maps

This paper proposes an end-to-end Learnable Edge-Attention Map (LEAM) method to assist image inpainting. To achieve a better-recovered effect, we design an edge attention module, which extracts the feature information of the edge map and re-normalizes the image feature information when automatically updating the edge map. And the information of known regions is adopted to assist the decoder generates semantically consistent results. A dual-discriminator structure consisting of the local discriminator and global discriminator is proposed to generate realistic texture details and improve the consistency of the overall structure. Experiments show that our method can obtain higher image inpainting quality than the existing state-of-the-art approaches, which improves PSNR by 3.58%, SSIM by 2.27%, and reduce MAE by 9.21% on average.
Neural network technology adaptation to the small-size objects identification in satellite images of insufficient resolution within the graphic reference images database

A novel flowchart for small-size objects identification in satellite images of insufficient resolution within the graphic reference images database using neural network technology based on compromise contradiction, i.e. simultaneously the resolution enhancement of the object segment of input image and the resolution reduction of the reference image to joint resolution through the simulation of the imaging system has been proposed. This is necessary due to a significant discrepancy between the resolutions of the input image and the graphic reference images used for identification. The required level of resolution enhancement for satellite images, as a rule, is unattainable, and a significant coarsening of reference images is undesirable because of identification errors. Therefore, a certain intermediate spatial resolution is used for identification, which, on the one hand, can be obtained, and on the other the loss of information contained in the reference image is still acceptable. The intermediate resolution is determined by simulating the process of image acquisition with satellite imaging system. To facilitate such simulation, it is advisable to perform it in the frequency domain, where the advanced Fourier analysis is available and, as a rule, all the necessary transfer properties of the links of image formation chain are known. Three main functional elements are engaged for identification: an artificial neural network for the resolution enhancement of input images, a module of frequency-domain simulating of the graphical reference satellite imaging and an artificial neural network for comparing the enhanced object segment with the reference model images. The feasibility of the described approach is demonstrated by the example of successful identification of the sea vessel image in the SPOT-7 satellite image. Currently, the works are under way to compare the performance of a neural network platforms variety for small-size objects identification in satellite images aa well as to assess achievable accuracy.
Forecasting Gas and Oil Potential of Subsoil Plots via Co-analysis of Satellite, Geological, Geophysical and Geochemical Information by Means of Subjective Logic

The purpose of the paper is to substantiate a new approach to forecasting hydrocarbon potential of subsoil plots via co-analysis of satellite, geo-geophysical and geochemical information by means of subjective logic. Basic concepts of subjective logic are given, the method of forecasting the oil and gas potential of a subsoil plots is described. The method is tested by applying it to real area with hydrocarbon deposits.
Digital wisdom in research work

Purpose/Thesis&#x0D; Digital wisdom is understood as the ability to use modern computer technology wisely to improve thinking and decision-making and to share results (Prensky, 2012a, p. 47). The aim of this article is to present selected digital tools that contribute to increasing the effectiveness of scientific research and facilitate conceptual work, information retrieval, note taking and the writing up of research.&#x0D; Approach/Methods&#x0D; The basic stages of the research process were distinguished, and for each of them a tool (a computer program) was selected that directly increases work efficiency and features functions that exceed human capabilities.&#x0D; Results and conclusions&#x0D; The latest digital tools are equipped with features that improve effectiveness at all stages of the research process. The improvement includes both speeding up completion of a task (e.g. searching for information) and showing relationships between documents or individual concepts that are barely noticeable by human standards (e.g. through the visualization of data and information).&#x0D; Originality/Value&#x0D; The skillful use of modern digital tools is a fundamental element of digital wisdom as computer programs and web applications significantly broaden human perception and cognitive capabilities.
Deconstructing the Impact of Entrepreneurship on Income Inequality in Sub-Saharan Africa Countries

Abstract The study examines the impacts of entrepreneurship on income inequality in a panel of 29 Sub-Saharan African countries spanning from 2004 to 2020. The paper employs a dynamic heterogeneous panel approach to differentiate between long-run and short-run impacts of entrepreneurship on income inequality. The findings establish a robust and direct nexus between entrepreneurial activities and income disparity. The results of the two entrepreneurial indicators are stable. Besides, the coefficient of the human capital is positive in the regression and statistically significant at a 5 percent significance level. The proxies for macroeconomic factors exhibit diverse signs and impact, which suggest a policy stimulus aimed at refining macroeconomic situations and also ignite prospects for households to increase their incomes.
Strategies for Improving the Effectiveness of English Translation Teaching in Higher Vocational Colleges Based on Data Mining

Abstract At present, most vocational colleges have set up English majors accordingly, and translation is a compulsory course for English majors. Although some concrete measures have been taken to implement the teaching mode and method of translation teaching, there are still some problems in the practice of translation teaching. Higher vocational students have some misunderstandings in English translation, which requires teachers to improve teaching methods and promote students’ initiative to participate in teaching. How to change the position of translation teaching in higher vocational English teaching and improve the level of English translation teaching is a hot issue for all English teachers. Based on an in-depth analysis of the characteristics of English translation teaching in higher vocational colleges, this paper expounds in detail the strategies for improving the effectiveness of English translation teaching in higher vocational colleges based on data mining.
Template Attacks against ECC: practical implementation against Curve25519

This paper introduces a new profiling attack that targets elliptic curves-based cryptographic implementations. This attack exploits leakages from the conditional swap operation used in implementations using the Montgomery Ladder as a scalar multiplication method for calculating kP in constant time. In addition, our attack requires only one attack trace. This paper shows how the attack is performed on the mbedTLS Curve25519 function and why conventional coordinates randomization countermeasures do not prevent this type of attack. Then, a new countermeasure that is efficient against the presented attack will be proposed and tested. This work was carried out on the implementation of mbedTLS from Curve25519.
Intrinsically typed compilation with nameless labels

To avoid compilation errors it is desirable to verify that a compiler is type correct —i.e., given well-typed source code, it always outputs well-typed target code. This can be done intrinsically by implementing it as a function in a dependently typed programming language, such as Agda. This function manipulates data types of well-typed source and target programs, and is therefore type correct by construction. A key challenge in implementing an intrinsically typed compiler is the representation of labels in bytecode. Because label names are global, bytecode typing appears to be inherently a non-compositional, whole-program property. The individual operations of the compiler do not preserve this property, which requires the programmer to reason about labels, which spoils the compiler definition with proof terms. In this paper, we address this problem using a new nameless and co-contextual representation of typed global label binding, which is compositional. Our key idea is to use linearity to ensure that all labels are defined exactly once. To write concise compilers that manipulate programs in our representation, we develop a linear, dependently typed, shallowly embedded language in Agda, based on separation logic. We show that this language enables the concise specification and implementation of intrinsically typed operations on bytecode, culminating in an intrinsically typed compiler for a language with structured control-flow.
SHIKEBLCU at SemEval-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment

Lexical entailment recognition plays an important role in tasks like Question Answering and Machine Translation.As important branches of lexical entailment, predicting multilingual and cross-lingual lexical entailment (LE) are two subtasks of SemEval2020 Task2.In previous monolingual LE studies, researchers leverage external linguistic constraints to transform word embeddings for LE relation.In our system, we expand the number of external constraints in multiple languages to obtain more specialised multilingual word embeddings.For the cross-lingual subtask, we apply a bilingual word embeddings mapping method in the model.The mapping method takes specialised embeddings as inputs and is able to retain the embeddings' LE features after operations.Our results for multilingual subtask are about 20% and 10% higher than the baseline in graded and binary prediction respectively.
On Relating 'Why?' and 'Why Not?' Explanations

Explanations of Machine Learning (ML) models often address a 'Why?' question. Such explanations can be related with selecting feature-value pairs which are sufficient for the prediction. Recent work has investigated explanations that address a 'Why Not?' question, i.e. finding a change of feature values that guarantee a change of prediction. Given their goals, these two forms of explaining predictions of ML models appear to be mostly unrelated. However, this paper demonstrates otherwise, and establishes a rigorous formal relationship between 'Why?' and 'Why Not?' explanations. Concretely, the paper proves that, for any given instance, 'Why?' explanations are minimal hitting sets of 'Why Not?' explanations and vice-versa. Furthermore, the paper devises novel algorithms for extracting and enumerating both forms of explanations.
DenseYOLO: Yet Faster, Lighter and More Accurate YOLO

As much as an object detector should be accurate, it should be light and fast as well. However, current object detectors tend to be either inaccurate when lightweight or very slow and heavy when accurate. Accordingly, determining tolerable tradeoff between speed and accuracy of an object detector is not a simple task. One of the object detectors that have commendable balance of speed and accuracy is YOLOv2. YOLOv2 performs detection by dividing an input image into grids and training each grid cell to predict certain number of objects. In this paper we propose a new approach to even make YOLOv2 more fast and accurate. We re-purpose YOLOv2 into a dense object detector by using fine-grained grids, where a cell predicts only one object and its corresponding class and objectness confidence score. Our approach also trains the system to learn to pick a best fitting anchor box instead of the fixed anchor assignment during ground-truth annotation as used by YOLOv2. We will also introduce a new loss function to balance the overwhelming imbalance between the number of grids responsible of detecting an object and those that should not.
Classical University Crisis: New Challenges and Prospects of Russian Higher Education

The objective of the paper is to discuss the most crucial problem, related to “winding down” of the Russian Liberal Arts education due to its economic inefficiency. The methodology includes the descriptive scientific-research method, which involves the analysis of scientific sources, official provisions, mass-media publications and the students’ questionnaire. The authors came to the following conclusions: 1. The model of education, which is guided by the logic of economic growth, and not by the logic of human development, leads to a general dehumanization of education and society. 2. The process of education “dehumanizing” is manifested in the following destructive trends: pragmatic utility and convenience of gaining knowledge; the dominance of technical knowledge and the transfer of humanitarian disciplines into optional courses; the advance of online education; the imperfection of university lecturers’ ratings and their transformation into a bureaucratic evaluation system; new types of inequality in education. 3. In Russia, the above-mentioned trends are aggravated by the abnormal establishment of market economy; the verticalization of power and the lack of professionalism in university management; social sphere underfunding; the aggressive implementation of the Bologna system. 4. The current crisis in education is accompanied by the large-scale emigration of gifted students and lecturers. The authors recommend to consider the University as a classical “home” of formation, preservation and transfer of culture; online courses should be seen as a new format of teaching aids, as well as a technological tool for retraining and for people with disabilities.
The Grammatical Error Analysis Found in Students’ Composition

Grammar is an important language component to enhance students’ language proficiency. However, many students still make grammatical errors in writing their essays. This research aims at analyzing types of grammatical errors found in students’ essays. The type of this research is a descriptive case study. The subjects of the study were 20 third-semester students of Purworejo Muhammadiyah University, Indonesia. The researchers used a test to collect the data. The results of tests were analyzed descriptively by using Keshavarz’s theory. The analysis result shows that the percentage for each error type is 34.06% (omission), 7.25% (addition), 57.97% (substitution), and 0.72% (permutation). Based on the research result, the researchers conclude that the most dominant error is substitution. The percentage of error can prove it, that is, 57.97%. There are some implications of error analysis in English language teaching in universities in Indonesia. The lecturer can give enrichment, understand students’ grammar competence, give corrective feedback to students’ errors, modify target language learning items in classrooms and textbooks, understand the way students apply the target language rules, and use the effective teaching method or learning media.
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation

Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training. The teacher network is supposed to be a trustworthy predictor and the student tries to mimic its predictions. Usually, a student with a lighter architecture is selected so we can achieve compression and yet deliver high-quality results. In such a setting, distillation only happens for final predictions whereas the student could also benefit from teacher's supervision for internal components. Motivated by this, we studied the problem of distillation for intermediate layers. Since there might not be a one-to-one alignment between student and teacher layers, existing techniques skip some teacher layers and only distill from a subset of them. This shortcoming directly impacts quality, so we instead propose a combinatorial technique which relies on attention. Our model fuses teacher-side information and takes each layer's significance into consideration, then performs distillation between combined teacher layers and those of the student. Using our technique, we distilled a 12-layer BERT (Devlin et al. 2019) into 6-, 4-, and 2-layer counterparts and evaluated them on GLUE tasks (Wang et al. 2018). Experimental results show that our combinatorial approach is able to outperform other existing techniques.
Smart Border Security System Using Internet of Things

National security and defence is incredibly important for a country and its people. For increasing tension in the border areas due to unresolved conflicts, currently national security systems emphasize more on border security to protect the country from terrorist attacks, illegal border crossing and infiltration from the neighbouring countries. To make security system more efficient, a real time border security system is needed which can provide 24 h surveillance in the border areas with high accuracy and that can minimize the need of human involvement by utilizing the most advanced sensors and actuators. Indian Border Guarding forces are already installing and adopting newer technologies in terms of cameras, night vision devices, radars etc. But for the efficient and intelligent use of collected data, involvement of modern and innovative technology like Internet of Things (IoT) is very necessary, which already has been adopted but in very small scale and in limited areas. Whereas, it is the reliable source of accurate data and renowned for smart and fast decision making as it is one of the major fields of implementing Big Data and Analytics. So, a smart IoT based solution has been introduced for securing hazardous border areas with extreme climatic conditions, diverse land forms, river terrains, inaccessible dense forest areas which is very tough to monitor for the individual. This paper “Smart Border Security System using Internet of Things” proposes a low-cost system that uses various sensors like Passive Infrared (PIR) sensor and OV7670 camera module to sense movement of any object within a range and capture images of intruder respectively. The system can upload the sensed data into a cloud server which can be retrieved in a base station by using web and desktop application as well. The system can also send alert to the base station by processing the sensed data. Also, it allows user i.e. the trained security personnel to control the camera and retrieve data from it from a distant. Through the proposed system it is possible to detect the intruder crossing the border area instantly.
A Learning Rate Method for Full-Batch Gradient Descent

Abstract In this paper, we present a learning rate method for gradient descent using only first order information. This method requires no manual tuning of the learning rate. We applied this method on a linear neural network built from scratch, along with the full-batch gradient descent, where we calculated the gradients for the whole dataset to perform one parameter update. We tested the method on a moderate sized dataset of housing information and compared the result with that of the Adam optimizer used with a sequential neural network model from Keras. The comparison shows that our method finds the minimum in a much fewer number of epochs than does Adam.
Interpretive Structural Modelling to assess the enablers of blockchain technology in supply chain

The purpose of this paper is to identify, analyze the enablers for blockchain technology in supply chain. We have proposed Interpretive Structural Modeling (ISM) to analyze the different types of enablers like autonomous enablers, dependent enablers, linkage enablers and driver enablers from SSIM (Structural Self-Interaction Matrix) in MATLAB software by using partition level and iterations for the prioritization of enablers. We have prioritized the different enablers and proposed cluster diagram of enablers for blockchain technology in supply chain. On the basis of our analysis, we have formed five clusters and found that traceability transparency, seamless connectivity, verifiability of transaction enablers are highly driven and dependent on the other input variables included in the supply chain system. The present work suggested the platform for both academicians and researchers to understand the relationship between enablers of blockchain technology in supply chain. This paper also provides the future direction to the practitioners for optimally assign the efforts and available resources to increase the current performance of supply chain system. This article prioritizes the enablers of block chain in clusters according to their level of impact.
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering

Commonsense reasoning refers to the ability of evaluating a social situation and acting accordingly.Identification of the implicit causes and effects of a social context is the driving capability which can enable machines to perform commonsense reasoning.The dynamic world of social interactions requires context-dependent on-demand systems to infer such underlying information.However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations.Hence they fail to estimate the correct reasoning path.In this paper, we present Conditional SEQ2SEQ-based Mixture model (COSMO), which provides us with the capabilities of dynamic and diverse content generation.We use COSMO to generate context-dependent clauses, which form a dynamic Knowledge Graph (KG) on-the-fly for commonsense reasoning.To show the adaptability of our model to context-dependant knowledge generation, we address the task of zero-shot commonsense question answering.The empirical results indicate an improvement of up to +5.2% over the state-of-the-art models.