Reviewerly/exHarmony huggingface dataset:
Detail-attention convolutional neural network for meter recognition

<p indent=0mm>The classical meter recognition method is severely affected by light and noise interference in the recognition of meters, which limits its generalization ability. Recent methods based on convolutional neural networks cannot handle the diversity of meters via detection of the key points (center of circle, maximum and minimum range). In this study, we investigate the recognition of a mechanical meter. According to the manual reading paradigm, the scale and pointer are first positioned, and the reading value is then determined based on the relative position of the pointer and scale, which is compatible with different meters. To extract the tick marks of meters in a stable and precise scheme, Meter scan net (MSN) is designed based on the low-occupancy features of the meter’s tick area to predict the heatmap. Utilizing our proposed Detail-Attention module, MSN can greatly retain detailed features and focus on the tick area, which is helpful for the final segmentation. Post-processing includes using the output of MSN to perform circle fitting to obtain the scale arc, sampling along the normal phase direction of the scale arc, and locating the scale and pointer position. To handle cases of missing tick marks, a frequency-based analysis method is proposed to restore the missing tick marks, which enlarges the application scope of our method. Finally, extensive experiments are conducted that reveal that the proposed method has high accuracy and is compatible with different types of meters.
The Influence of the Mastery of Function Words on Students’ Reading Ability

This study is an ex-post facto research which aims to find out the influence of the mastery of function words on reading ability of the seventh grade students of secondary school and to find out how the reading ability between students with adequate function words’ mastery and students with inadequate function words’ mastery is. The samples used in this research were two groups of students who have adequate and inadequate function words’ mastery. Each group consists of 30 students that is chosen by using stratified random sampling. In analysing the data, researcher used Independent Sample t-Test. The result shows that the mastery of function words significantly influences students’ reading ability. It is found that there is significant difference of students’ reading ability between students with adequate mastery in function words with those having inadequate mastery in function words. It is found that students with adequate function words’ mastery have better and higher ability in reading than students having inadequate function words’ mastery.
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text

In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.
Whose side are ethics codes on?: power, responsibility and the social good

The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the good, of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes..
A Study on the Validity of the Grammar Fill-in-the-blank Questions of the National English Paper for College Entrance Examination in 2020

Grammar fill-in-the-blank is different from previous single-choice questions. It uses text as a carrier to test students’ English ability, instead of traditional multiple-choice questions. However, is it better than the single-choice test? Is it effective? Taking the grammar filling-in-the-blank questions of the 2020 National College Entrance Examination Papers 1, 2 and 3 as the research object, starting from the grammar filling-in-the-blank, combined with the students’ expected answers, this article conducts an analysis and investigation. The results show that grammar fill-in-the-blank questions are highly effective, which can examine students’ grammatical ability.. However, some test points are still limited to the level of words or phrases, so the discourse function is not fully utilized. In this regard, the author puts forward specific suggestions for improvement.
IAI MovieBot: A Conversational Movie Recommender System

Conversational recommender systems support users in accomplishing recommendation-related goals via multi-turn conversations. To better model dynamically changing user preferences and provide the community with a reusable development framework, we introduce IAI MovieBot, a conversational recommender system for movies. It features a task-specific dialogue flow, a multi-modal chat interface, and an effective way to deal with dynamically changing user preferences. The system is made available open source and is operated as a channel on Telegram.
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question. In this paper we explore how Blockchains (in particular Ethereum) can be used to determine the evolving ownership of a model trained with Federated Learning. Firstly, we use the step-by-step evaluation metric to assess the relative contributivities of participants in a Federated Learning process. Next, we introduce 2CP, a framework comprising two novel protocols for Blockchained Federated Learning, which both reward contributors with shares in the final model based on their relative contributivity. The Crowdsource Protocol allows an actor to bring a model forward for training, and use their own data to evaluate the contributions made to it. Potential trainers are guaranteed a fair share of the resulting model, even in a trustless setting. The Consortium Protocol gives trainers the same guarantee even when no party owns the initial model and no evaluator is available. We conduct experiments with the MNIST dataset that reveal sound contributivity scores resulting from both Protocols by rewarding larger datasets with greater shares in the model. Our experiments also showed the necessity to pair 2CP with a robust model aggregation mechanism to discard low quality inputs coming from model poisoning attacks.
Type-two Iteration with Bounded Query Revision

Motivated by recent results of Kapron and Steinberg (LICS 2018) we introduce new forms of iteration on length in the setting of applied lambda-calculi for higher-type poly-time computability. In particular, in a type-two setting, we consider functionals which capture iteration on input length which bound interaction with the type-one input parameter, by restricting to a constant either the number of times the function parameter may return a value of increasing size, or the number of times the function parameter may be applied to an argument of increasing size. We prove that for any constant bound, the iterators obtained are equivalent, with respect to lambda-definability over type-one poly-time functions, to the recursor of Cook and Urquhart which captures Cobham's notion of limited recursion on notation in this setting.
Heat Prediction of High Energy Physical Data Based on LSTM Recurrent Neural Network

High-energy physics computing is a typical data-intensive calculation. Each year, petabytes of data needs to be analyzed, and data access performance is increasingly demanding. The tiered storage system scheme for building a unified namespace has been widely adopted. Generally, data is stored on storage devices with different performances and different prices according to different access frequency. When the heat of the data changes, the data is then migrated to the appropriate storage tier. At present, heuristic algorithms based on artificial experience are widely used in data heat prediction. Due to the differences in computing models of different users, the accuracy of prediction is low. A method for predicting future access popularity based on file access characteristics with the help of LSTM deep learning algorithm is proposed as the basis for data migration in hierarchical storage. This paper uses the real data of high-energy physics experiment LHAASO as an example for comparative testing. The results show that under the same test conditions, the model has higher prediction accuracy and stronger applicability than existing prediction models.
MAGIC: Learning Macro-Actions for Online POMDP Planning using Generator-Critic.

When robots operate in the real-world, they need to handle uncertainties in sensing, acting, and the environment. Many tasks also require reasoning about long-term consequences of robot decisions. The partially observable Markov decision process (POMDP) offers a principled approach for planning under uncertainty. However, its computational complexity grows exponentially with the planning horizon. We propose to use temporally-extended macro-actions to cut down the effective planning horizon and thus the exponential factor of the complexity. We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator from data, and uses the learned macro-actions to perform long-horizon planning. MAGIC learns the generator using experience provided by an online planner, and in-turn conditions the planner using the generated macro-actions. We evaluate MAGIC on several long-term planning tasks, showing that it significantly outperforms planning using primitive actions, hand-crafted macro-actions, as well as naive reinforcement learning in both simulation and on a real robot.
Summoning, No-Signalling and Relativistic Bit Commitments

Summoning is a task between two parties, Alice and Bob, with distributed networks of agents in space-time. Bob gives Alice a random quantum state, known to him but not her, at some point. She is required to return the state at some later point, belonging to a subset defined by communications received from Bob at other points. Many results about summoning, including the impossibility of unrestricted summoning tasks and the necessary conditions for specific types of summoning tasks to be possible, follow directly from the quantum no-cloning theorem and the relativistic no-superluminal-signalling principle. The impossibility of cloning devices can be derived from the impossibility of superluminal signalling and the projection postulate, together with assumptions about the devices’ location-independent functioning. In this qualified sense, known summoning results follow from the causal structure of space-time and the properties of quantum measurements. Bounds on the fidelity of approximate cloning can be similarly derived. Bit commitment protocols and other cryptographic protocols based on the no-summoning theorem can thus be proven secure against some classes of post-quantum but non-signalling adversaries.
Bridging the Technology Gap between Industry and Semantic Web: Generating Databases and Server Code from RDF

Despite great advances in the area of Semantic Web, industry rather seldom adopts Semantic Web technologies and their storage and query concepts. Instead, relational databases (RDB) are often deployed to store business-critical data, which are accessed via REST interfaces. Yet, some enterprises would greatly benefit from Semantic Web related datasets which are usually represented with the Resource Description Framework (RDF). To bridge this technology gap, we propose a fully automatic approach that generates suitable RDB models with REST APIs to access them. In our evaluation, generated databases from different RDF datasets are examined and compared. Our findings show that the databases sufficiently reflect their counterparts while the API is able to reproduce rather simple SPARQL queries. Potentials for improvements are identified, for example, the reduction of data redundancies in generated databases.
A Methodological Approach to Model CBR-Based Systems

Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging to use algorithmic and heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited in domains like management, medicine, design, construction, retail and smart grid. CBR is a technique for problem-solving and captures new knowledge by using past experiences. One of the main CBR deployment challenges is the target system modeling process. This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models. Splitting the modeling process with two models facilitates the allocation of expertise between the application domain and the CBR technology. The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.
Linear Separation via Optimism

Binary linear classification has been explored since the very early days of the machine learning literature. Perhaps the most classical algorithm is the Perceptron, where a weight vector used to classify examples is maintained, and additive updates are made as incorrect examples are discovered. The Perceptron has been thoroughly studied and several versions have been proposed over many decades. The key theoretical fact about the Perceptron is that, so long as a perfect linear classifier exists with some margin $γ&gt; 0$, the number of required updates to find such a perfect linear separator is bounded by $\frac{1}{γ^2}$. What has never been fully addressed is: does there exist an algorithm that can achieve this with fewer updates? In this paper we answer this in the affirmative: we propose the Optimistic Perceptron algorithm, a simple procedure that finds a separating hyperplane in no more than $\frac{1}γ$ updates. We also show experimentally that this procedure can significantly outperform Perceptron.
Prediction of Rooftop Photovoltaic Power Generation Using Artificial Neural Network

Photovoltaic (PV) system is known as one of the most popular renewable energy types in generating electricity power. However, one of the drawbacks of PV system is that the performance of PV panel output is incompatible and affected due to changing climate condition. Hence, it is important to predict the optimal power output of PV system. This study will cover the implementation of PV system at the rooftop of Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia. Then, the optimal PV output power on the rooftop will be predicted using calculation method and Artificial Neural Network (ANN). The results have shown that ANN has the ability to predict the PV output close to the calculation method.
COVID-19 IMPACT ON THE USE OF ICT TOOLS IN THE CROATIAN EDUCATION SYSTEM

Appears in: ICERI2020 Proceedings Publication year: 2020Pages: 5781-5786ISBN: 978-84-09-24232-0ISSN: 2340-1095doi: 10.21125/iceri.2020.1241Conference name: 13th annual International Conference of Education, Research and InnovationDates: 9-10 November, 2020Location: Online Conference
Local hybrid optical encryption system based on double random phase encoding

Most of the existing selective encryption schemes are based on image processing and cannot be realized by optical structures, so their practicality is limited. Combining the optical design, a local hybrid optical encryption system based on double random phase encoding is proposed. The system proposed in this paper possesses a common aperture and dual optical path structure, which is widely used in optical design and can effectively improve the practicality of optical encryption system. First, important information and non-important information in the original image are separated by a selective beam splitter. Then light waves carrying important information enter into the 4&lt;i&gt;f&lt;/i&gt; system for encryption, and light waves carrying non-important information enter into the diffraction system for encryption. Finally, part of the diffraction system ciphertext is replaced with 4&lt;i&gt;f&lt;/i&gt; system ciphertext to obtain the final encrypted image. Decryption is the reverse process of encryption. First, the 4&lt;i&gt;f&lt;/i&gt; system ciphertext is cut out from the final ciphertext. Then the 4&lt;i&gt;f&lt;/i&gt; system ciphertext is used to restore the information replaced in the diffraction system ciphertext, thereby obtaining the complete diffraction system ciphertext. Finally, the two ciphertexts go through the reverse process of their respective systems to complete the decryption. By comparing the statistical characteristics and mean square error of the original image and the encrypted image, the effectiveness of the proposed system's encryption process is proved. By analyzing the peak signal-to-noise ratio of the original image and the decrypted image, the effectiveness of the proposed system's decryption process is proved. The sensitivity of each key of the system is analyzed to prove the security of the system. Especially the system is highly sensitive to selective encryption key, which proves the effectiveness and security of the proposed system for selective encryption. Through simulation, it is verified that the proposed system is very resistant to diffraction attacks. Even if he can obtain all the diffraction keys, the attacker still cannot obtain the selectively encrypted information. Finally, through simulation, it is verified that the proposed system has good noise resistance and crop resistance, and high robustness as well. The proposed system can realize the selective encryption through optical structure, which is safe, effective and highly robust, and thus improving the practicality of selective optical encryption system.
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks.

We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedMM. By using user-defined functions, FusedMM can capture almost all computational patterns needed by popular graph embedding and GNN approaches. FusedMM is an order of magnitude faster than its equivalent kernels in Deep Graph Library. The superior performance of FusedMM comes from the low-level vectorized kernels, a suitable load balancing scheme and an efficient utilization of the memory bandwidth. FusedMM can tune its performance using a code generator and perform equally well on Intel, AMD and ARM processors. FusedMM speeds up an end-to-end graph embedding algorithm by up to 28x on different processors.
Information Policy Of The Educational Organizations On The Internet

The effective solution of social and legislative tasks of information openness of educational institutions leading role played by purposeful information policy, which develops and implements each educational organization. One of the problems that preventing effective solution is a limited vision of the leaders and teachers of the tasks of information openness. It is limited by the need to perform only the state requirements to the official website of the educational institutions in the practice of the majority of educational institutions. This situation is explained by the fact that in the minds of the majority of directors and teachers has not formed a new mental model of the system for educational institutions public information activities. It occupies a special place information policy, are guided first and foremost on the needs of developing social and professional needs in a variety of information on the activities of schools, kindergartens, children's supplementary education institutions, etc. The paper presents an approach, that changes the prevailing practice in the vision system in which the backbone element are not state requirements for transparency, and integrated information policy connected with the objectives of informing on the activities of educational organizations, transparency (including state requirements) and information cultures. Highlighting information policy as a system-forming element, identified five major directions. Following these directions in every educational organization can be formed and implemented an integrated information policy that raises public information activities of educational organizations to a new level.
Automation of Processing Multichannel Remote Sensing Images Based on Performance Prediction

This paper deals with processing of multichannel remote sensing images for which it is difficult and often impossible to carry out any stage of data processing in an interactive manner and, thus, full or, at least, partial automation is needed. Despite of existence of many methods, it is difficult to choose the best or an appropriate one and/or to set its parameters. One reason is that quantitative criteria characterizing efficiency of applicable methods are often unknown, especially for remote sensing data at hand. Thus, it is desirable to have some preliminary prediction of errors or accuracy for data processing methods and to choose a good method and its parameters properly. Here we consider few approaches and describe the results already obtained in recent years.