Reviewerly/exHarmony huggingface dataset:
Flow-Sensitive Type-Based Heap Cloning.

By respecting program control-flow, flow-sensitive pointer analysis promises more precise results than its flow-insensitive counterpart. However, existing heap abstractions for C and C++ flow-sensitive pointer analyses model the heap by creating a single abstract heap object for each memory allocation. Two runtime heap objects which originate from the same allocation site are imprecisely modelled using one abstract object, which makes them share the same imprecise points-to sets and thus reduces the benefit of analysing heap objects flow-sensitively. On the other hand, equipping flow-sensitive analysis with context-sensitivity, whereby an abstract heap object would be created (cloned) per calling context, can yield a more precise heap model, but at the cost of uncontrollable analysis overhead when analysing larger programs.This paper presents TypeClone, a new type-based heap model for flow-sensitive analysis. Our key insight is to differentiate concrete heap objects lazily using type information at use sites within the program control-flow (e.g., when accessed via pointer dereferencing) for programs which conform to the strict aliasing rules set out by the C and C++ standards. The novelty of TypeClone lies in its lazy heap cloning: an untyped abstract heap object created at an allocation site is killed and replaced with a new object (i.e. a clone), uniquely identified by the type information at its use site, for flow-sensitive points-to propagation. Thus, heap cloning can be performed within a flow-sensitive analysis without the need for context-sensitivity. Moreover, TypeClone supports new kinds of strong updates for flow-sensitive analysis where heap objects are filtered out from imprecise points-to relations at object use sites according to the strict aliasing rules. Our method is neither strictly superior nor inferior to context-sensitive heap cloning, but rather, represents a new dimension that achieves a sweet spot between precision and efficiency. We evaluate our analysis by comparing TypeClone with state-of-the-art sparse flow-sensitive points-to analysis using the 12 largest programs in GNU Coreutils. Our experimental results also confirm that TypeClone is more precise than flow-sensitive pointer analysis and is able to, on average, answer over 15% more alias queries with a no-alias result.
AlTwo: Vehicle Recognition in Foggy Weather Based on Two-Step Recognition Algorithm

This paper focuses on the application of vehicle recognition in foggy weather, and proposes a two-step recognition algorithm based on deep learning, hoping to still have a good recognition result under the influence of fog. In AlOne, we use the current popular defogging algorithms: adaptive histogram equalization, single-scale Retinex, dark channel priori for image defogging. In AlTwo, we build a convolutional neural network model based on AlexNet to recognize vehicle image and obtain prediction accuracy. By comparing different performance indicators, the best performing dark channel prior algorithm is selected as the defogging algorithm. In the end, we improved the accuracy of vehicle recognition under the influence of low, medium and high fog concentrations to more than 97%.
Artificial Neural Network Based Solar Radiation Estimation of Algeria Southwest Cities

Solar radiation estimation is the most integral part of design and performance of solar energy applications. Our paper aims to develop an artificial neural networks-based model for predicting the daily global solar radiation in three cities (Bechar, Naâma and Tindouf) in the south-west region of Algeria. Models’ inputs are: average temperature, wind speed, relative humidity, atmospheric pressure, extraterrestrial solar irradiation, sunshine duration. Three ANN multilayer architectures connection are used with the Levenberg-Marquardt algorithm for training. Efficiency of models was assessed using statistical tests including, correlation coefficient (R), root mean squared error (RMSE), mean bias error (MBE) and mean absolute percentage error (MAPE).The results during five years showed that, the Cascade-forward Neural Network (CFNN) and Feed-forward neural network (FFNN) models gives much better forecast of daily global solar radiation in the three Saharan cities. The models developed can be used for design and sizing solar energy systems, where radiation measuring stations are scarce in Algeria.
Vasiliy Davydov: The Founder of Activity Practice in Education

The paper analyzes the conceptual framework of the activity-based content of education that was elaborated by Vasiliy Davydov and implemented in a new design of school subjects. The article shows that, in order to promote activity practice in education, Davydov had to develop three project-oriented notions that he integrated into a single system of activity practice: 1. The notion of the structure of the thinking act – the ‘cell’ of any thinking processes acquired by students; 2. The notion of the concept as a system of actions (i.e. the concepts from school subjects, such as ‘number’, ‘phoneme’) that represents the main content of primary education; 3. The notion of a schoolchild’s learning activity as a form of activity aimed at mastering new types of school subjects. The integration of these three notions was possible due to Davydov’s metacognitive approach that allows one to analyze the concretization of the notions about the thinking act using the materials from different school subjects, in different types of activity. The paper argues that the next step in the development of activity practice in education requires the elaboration of the concept of education as a form of integration of the student’s learning activity and the teacher’s instructional activity.
Amplitude Estimation Algorithms and Implementations on Noisy Intermediate-Scale Quantum Devices

We implement two recently published quantum amplitude estimation algorithms on the IBM quantum device to analyze and discuss advantages of each of these algorithms from the perspective of its performance on a quantum device.
Coastal Image Captioning

Yang, Q.; Wang, G.; Zhang, X.; Grecos, C., and Ren, P., 2020. Coastal image captioning. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 145-150. Coconut Creek (Florida), ISSN 0749-0208.Coastal images convey immense semantic information of the corresponding coastal areas. This article presents a preliminary approach to coastal image captioning that describes salient semantic information of coastal images with accurate and meaningful sentences. Specifically, this article exploits a state-of-the-art method referred to as self-critical sequence training for coastal image captioning. Firstly, a convolutional neural network (CNN) produces fixed-length features from the coastal images. Secondly, the fixed-length features are fed into a long short-term memory (LSTM) to generate captions, i.e., the accurate and meaningful sentences. Finally, the LSTM is optimized in the context of reinforcement learning. Experimental evaluation on the Moye island remote sensing dataset validates the effectiveness.
Relation of the ICT with neuroeducation, inclusion, pluriculturality and environmental education through a Confirmatory Factorial Analysis study

The present work aims to analyze the relationship of ICT with neuroeducation, educational inclusion, environmental education and pluriculturality through Confirmatory Factor Analysis (Structural Equation Model – SEM) of a created ad hoc and a validated and confirmed scale. For the search for answers, a non-experimental, descriptive, explanatory and correlational research process was carried out. The instrument used to collect the data was a Likert scale, which has been validated in content and with a good Cronbach's alpha (.825). The construct validity was carried out with an exploratory factorial analysis (KMO (.764), Bartlett (Sign.000), Determinant (2.891E-30)). The sample was of 292 students of Master’s Degree in Teacher Training from the University of Jaen (Spain). The Confirmatory Factorial Analysis (SEM) allowed us to conclude that this relationship is possible, with greater strength between education technologies and educational inclusion, and between the former and pluriculturality, and with less strength between environmental education and neuroeducation, what should be taken into account to implement in university training programs.
Using Knowledge Graphs to improve Hate Speech Detection

With the increasing cases of online hate speech, there is an urgent demand for better hate speech detection systems. In this paper, we utilize Knowledge Graphs (KGs) to improve hate speech detection. Our initial results shows that incorporating information from KG helps the classifier to improve the performance.
Dynamic Preference Logic meets iterated belief change: Representation results and postulates characterization

AGM's belief revision is one of the main paradigms in the study of belief change operations. Recently, several logics for belief and information change have been proposed in the literature and used to encode belief change operations in rich and expressive semantic frameworks. While the connections of AGM-like operations and their encoding in dynamic doxastic logics have been studied before by the work of Segerberg, most works on the area of Dynamic Epistemic Logics (DEL) have not, to our knowledge, attempted to use those logics as tools to investigate mathematical properties of belief change operators. This work investigates how Dynamic Preference Logic, a logic in the DEL family, can be used to study properties of dynamic belief change operators, focusing on well-known postulates of iterated belief change.
Meningkatkan Keterampilan Menulis Karya Ilmiah Mahasiswa Geografi Melalui Pembelajaran Berbasis SETS (Science, Environment, Technology, Society)

Writing is a process for expressing ideas. Facts in the field show that most students still think that writing is a difficult and insignificant thing. Another fact shows that the writing culture has shifted to a copy-paste culture. Therefore, a certain strategy is needed to stimulate students to overcome various difficulties in writing scientific papers. This study aims to improve students' scientific writing skills by applying SETS (Science, Environment, Technology, Society) based learning. The research method used was Classroom Action Research with the Kemmis and Mctaggart model. The research subjects were 14 students of the Geography Education Study Program, Faculty of Teacher Training and Education, Lambung Mangkurat University. The data analysis used was the interactive model of Miles and Huberman. The results showed that the average score of students' scientific writing skills in pre-action was 59.14% (sufficient category), 70.42% in Cycle I (good category), and 76.71% in Cycle II (good category). Based on the research results, it can be concluded that the application of SETS-based learning can improve scientific writing skills for students of the Geography Education Study Program, Faculty of Teacher Training and Education, University of Lambung Mangkurat.
ISO 9001:2015 Risk-based Thinking: A Framework using Fuzzy-Support Vector Machine

: The Risk-Based Thinking (RBT) is one of the distinct new features of the ISO 9001:2015. Interestingly, the standard does not prescribe any tools, hence, organizations are puzzled on the extent of conformance. Some organizations have adopted formal tools, however, these resources seem insufficient in linking the standard into an evidence-based decision-support system. To resolve the gaps in the RBT implementation, this paper proposes a framework based on Fuzzy Inference System (FIS) and Support Vector Machine (SVM) in order to automate the risk analysis and evaluation, proposal and verification of action plans, and predict the feasibility of risks and opportunities according to text patterns. Based on modelling results, the framework was found to have no significant difference in terms of accuracy compared to the conventional method. Both FIS-1 and FIS 2 models were found to be statistically significantly faster in computation than the conventional method by 3.26s and 1.15s, respectively. Meanwhile, the SVM model was found to have 97.16% classification accuracy and 97.4% confusion matrix rating during training, and 95% classification accuracy during testing. The results affirmed that FIS and SVM are efficient tools in feasibly conforming with the RBT requirements of the ISO 9001:2015 international standard.
Embracing uMunthu: How Informal Caregivers in Malawi Use ICTs

This paper explores the way informal caregivers in Malawi use different types of ICTs for the purposes of caring for their loved ones, supporting each other and building a community under adverse circumstances. We collected empirical material in the Mangochi district during a field study through a focus group and a workshop with informal caregivers and NGOs. For the purposes of our analysis, we draw from the uMunthu philosophy of Malawi in order to explore and understand the contextual conditions and the local phenomena. Our findings show that, the communal character of ICTs and their shareability often allows informal caregivers to come together as a community and overcome the scarcity of ICT resources through sharing, which is in line with the uMunthu principles. Quite often, though, sharing can lead to unintended consequences. The paper contributes to the ICT4D literature by theorising around the concept of sharing and shareability of ICTs; in doing so, we join our voices to the growing discussion on the use of southern theories and paradigms.
Choice Set Misspecification in Reward Inference

Specifying reward functions for robots that operate in environments without a natural reward signal can be challenging, and incorrectly specified rewards can incentivise degenerate or dangerous behavior. A promising alternative to manually specifying reward functions is to enable robots to infer them from human feedback, like demonstrations or corrections. To interpret this feedback, robots treat as approximately optimal a choice the person makes from a choice set, like the set of possible trajectories they could have demonstrated or possible corrections they could have made. In this work, we introduce the idea that the choice set itself might be difficult to specify, and analyze choice set misspecification: what happens as the robot makes incorrect assumptions about the set of choices from which the human selects their feedback. We propose a classification of different kinds of choice set misspecification, and show that these different classes lead to meaningful differences in the inferred reward and resulting performance. While we would normally expect misspecification to hurt, we find that certain kinds of misspecification are neither helpful nor harmful (in expectation). However, in other situations, misspecification can be extremely harmful, leading the robot to believe the opposite of what it should believe. We hope our results will allow for better prediction and response to the effects of misspecification in real-world reward inference.
Performance evaluation of differential privacy mechanisms in blockchain-based smart metering

In this chapter, the author first work over the integration of differential privacy and blockchain in real-time smart metering scenario. Afterwards, we carried out performance evaluation of four variants of differential privacy in the proposed blockchain-based smart metering scenario. The performance evaluation section of this chapter demonstrates that each privacy -preserving mechanism adds differently depending upon the selected privacy parameters and the input data. However, in the case of high peak values the Geometric mechanism surpasses other noise addition variants, although, in case of low peak values in metre reading, the Laplace mechanism performs better at ε = 0.01. As a part of future work, we are working over the integration of differential privacy and blockchain in other cyber-physical systems scenarios.
Multi-choice Relational Reasoning for Machine Reading Comprehension

This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR2) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children’s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.
Psychological and Educational Prerequisites for Primary Prevention of Drug Addiction among Students

The article deals with the organization of primary prevention of drug abuse in an institution of higher education. Effective psychological and educational prerequisites include: early detection of illegal use of narcotic drugs and psychotropic substances; raising students' awareness of the legal consequences of abusing narcotic drugs and psychotropic substances; informing all participants in educational process on how to obtain medical and social help in the regions they live (are educated); organizing of educational activities in a higher school aiming at creation of possibilities for students to take part in active prosocial work; development of psychological and educational competency of teachers and parents (or legal representatives) in the field of prevention of addictive behavior among the youth; arrangement, carrying out and methodological support for peer training; psychological assistance for students, including educational and developmental work, psychological help and support, as well as psychological counselling.
Learning with Retrospection

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarded information indeed useless? We argue that the discarded information can benefit the subsequent training. In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. LWR is a simple yet effective training framework to improve accuracies, calibration, and robustness of DNNs without introducing any additional network parameters or inference cost, but only with a negligible training overhead. Extensive experiments on several benchmark datasets demonstrate the superiority of LWR for training DNNs.
HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts

Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with.In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.
Scaled Population Subtraction for Approximate Computing

In this paper we present Scaled Population Subtraction to fill a void in Scaled Population arithmetic. Scaled population (SP) arithmetic is a scheme that is inspired by stochastic computing (SC), a non-conventional approximate computing method that is well known for its simplicity, area efficiency and resilience to bit errors. SP arithmetic reduces the numerical errors compared to SC and also solves the serialization limitation of SC, since it is designed to have a O(1) gate delay. Previously, SP was limited to only addition and multiplication and did not have a way to perform subtraction. This paper introduces the basic SP subtraction idea, followed by a detailed study of several ways that the basic design can be improved to reduce the computational error. Our best SP design significantly improves the error compared to our basic SP subtraction idea (reducing it by 32.3%). We also study the trade-off between design complexity of the SP subtractor against output error. Also, our implementation of the SP subtractor exhibits an improved delay, power and area compared to fixed point realizations with the same size.
Overview of the 7th Workshop on Asian Translation.

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.
Suffix Trees, DAWGs and CDAWGs for Forward and Backward Tries

The suffix tree, DAWG, and CDAWG are fundamental indexing structures of a string, with a number of applications in bioinformatics, information retrieval, data mining, etc. An edge-labeled rooted tree (trie) is a natural generalization of a string, which can also be seen as a compact representation of a set of strings. Kosaraju [FOCS 1989] proposed the suffix tree for a backward trie, where the strings in the trie are read in the leaf-to-root direction. In contrast to a backward trie, we call a usual trie as a forward trie. Despite a few follow-up works after Kosaraju’s paper, indexing forward/backward tries is not well understood yet. In this paper, we show a full perspective on the sizes of indexing structures such as suffix trees, DAWGs, and CDAWGs for forward and backward tries. In particular, we show that the size of the DAWG for a forward trie with n nodes is $$\varOmega (\sigma n)$$ , where $$\sigma $$ is the number of distinct characters in the trie. This becomes $$\varOmega (n^2)$$ for an alphabet of size $$\sigma = \varTheta (n)$$ . Still, we show that there is a compact O(n)-space implicit representation of the DAWG for a forward trie, whose space requirement is independent of the alphabet size. This compact representation allows for simulating each DAWG edge traversal in $$O(\log \sigma )$$ time, and can be constructed in O(n) time and space over any integer alphabet of size O(n).