Reviewerly/exHarmony huggingface dataset:
Pin or Fuse? Exploiting Scratchpad Memory to Reduce Off-Chip Data Transfer in DNN Accelerators

Growing interests in on-device AI have led to the proliferation of accelerators dedicated to neural network inference. Most ASIC accelerators are equipped with compiler-controlled scratchpad memory (SPM) used as a last-level cache to reduce the number of accesses to off-chip memory. A widely-used strategy for utilizing SPM is fused-layer execution, which divides a DNN model into groups of layers and forwards the intermediate results within each group without eviction to the off-chip memory. However, layer fusion has an inherent limitation that the fusion of consecutive layers increases the amount of computations, leading to sub-optimal performance.
D2X: An eXtensible conteXtual Debugger for Modern DSLs

Compiled Domain Specific Languages are taking over various high-performance domains because of their ability to exploit the domain knowledge and apply optimizations that produce the most specialized code. A lot of research has gone into making DSLs more performant and easy to prototype. But the Achilles heel for DSLs is still the lack of debugging support that provides an end-to-end picture to the user and improves the productivity of both the DSL designer and the end-user. Conventional techniques extend the compilers, the debugging information format, and the debuggers themselves to provide more information than what the debugger can provide when attached to the generated code. Such an approach quickly stops scaling as adding extensions to large and complex debuggers hampers DSL designer productivity. We present D2X, a DSL debugging infrastructure that works with most standard debuggers without any modifications and is easily extensible to capture all the domain specific information the end-user cares about. We show that we can add debugging support to the state-of-the-art graph DSL GraphIt with as little as 1.4% changes to the compiler code base. We also apply our techniques to a meta-programming DSL framework BuildIt so that any DSLs built on top of BuildIt get debugging support without any modifications further boosting the productivity of future DSL designers.
Designing Personas for E-Resources Users in the University Libraries

Persona is a method to create a user profile by describing a fictitious user through user experience. This persona study needs to be carried out for the benefit of system design according to the users’ wishes because, so far, electronic resources (e-resources) are not widely used due to cognitive and affective factors such as limited subscription resources, limited user manuals, limited navigation features, and frequent errors when using electronic resources. This leaves the user feeling confused and stressed. The aim of this study is to obtain profiles of e-resource users in college libraries. The method used is an empathy map created with data from 32 users who answered questionnaires and participated in interviews. This study found four e-resource user personas in university libraries: lecturers, students, research assistants, and librarians. Users want a guide for using electronic resources that allows for chat and sharing, and which is fun and can be accessed from any device anytime and anywhere. The benefits of this study will be useful for designing e-resource systems according to users’ wishes.
Automatic Identification and Mapping of Cone-Shaped Volcanoes Based on the Morphological Characteristics of Contour Lines

Cone-shaped volcanoes have important research significance and application value due to their typical cone shape and unique structural features. The existing methods for recognizing volcanoes are mainly morphological feature matching and machine learning. In general, the former has low recognition accuracy, while the latter requires a large number of training samples. The contour lines of cone-shaped volcanoes are distributed in concentric circles. Furthermore, from the center outwards, the elevation of the contour lines increases first and then decreases. Based on the morphological characteristics of cone-shaped volcanoes and the Hough transform algorithm, the main algorithm includes (1) preliminary filtering of contour lines, (2) filtering circular contour lines based on random Hough transform, (3) grouping contour lines based on contour trees, (4) recognizing cone-shaped volcanoes based on concentric-circle contour lines, and (5) automatically mapping cone-shaped volcanoes. Case studies demonstrate the effectiveness of this method for detecting cone-shaped volcanoes in the Western Galapagos shield volcanoes and the Mariana Trench submarine volcano group. The proposed algorithm has low missed and false alarm rates, which is basically consistent with the manual recognition results. This method can effectively automatically recognize cone-shaped volcanoes and cone-shaped landscapes and is a powerful means to support deep-space and deep-sea exploration.
Generative Transformer with Knowledge-Guided Decoding for Academic Knowledge Graph Completion

Academic knowledge graphs are essential resources and can be beneficial in widespread real-world applications. Most of the existing academic knowledge graphs are far from completion; thus, knowledge graph completion—the task of extending a knowledge graph with missing entities and relations—attracts many researchers. Most existing methods utilize low-dimensional embeddings to represent entities and relations and follow the discrimination paradigm for link prediction. However, discrimination approaches may suffer from the scaling issue during inference with large-scale academic knowledge graphs. In this paper, we propose a novel approach of a generative transformer with knowledge-guided decoding for academic knowledge graph completion. Specifically, we introduce generative academic knowledge graph pre-training with a transformer. Then, we propose knowledge-guided decoding, which leverages relevant knowledge in the training corpus as guidance for help. We conducted experiments on benchmark datasets for knowledge graph completion. The experimental results show that the proposed approach can achieve performance gains of 30 units of the MRR score over the baselines on the academic knowledge graph AIDA.
Engineering faster double‐array Aho–Corasick automata

Abstract Multiple pattern matching in strings is a fundamental problem in text processing applications such as regular expressions or tokenization. This article studies efficient implementations of double‐array Aho–Corasick automata (DAACs), data structures for quickly performing the multiple pattern matching. The practical performance of DAACs is improved by carefully designing the data structure, and many implementation techniques have been proposed thus far. A problem in DAACs is that comprehensive descriptions and experimental analyses on their ideas are not provided. Engineers face difficulties in implementing an efficient DAAC. In this article, we review implementation techniques for DAACs and provide a comprehensive description of them. We also propose several new techniques for further improvement. We conduct exhaustive experiments through real‐world datasets and reveal the best combination of techniques to achieve a higher performance in DAACs. The best combination is different from those used in the most popular libraries of DAACs, which demonstrates that their performance can be further enhanced. On the basis of our experimental analysis, we developed a new Rust library for fast multiple pattern matching using DAACs, named Daachorse , as open‐source software at https://github.com/daac‐tools/daachorse . Experiments demonstrate that Daachorse outperforms other AC‐automaton implementations, indicating its suitability as a fast alternative for multiple pattern matching in many applications.
The effect of interest and attitude on public comprehension of news with data visualization

In recent years, data visualization has been gaining space in journalism, not only in the specialized press, but also in the general press. The objective of this article is to analyze whether there are differences between the impact of receiving a traditional news item and that of a news item with data visualization, in terms of interest, comprehension and attitudes toward data visualization. For this, a study ( N = 700) was carried out with two experimental conditions (traditional news vs. news with data visualization), using scientific and health communication news. Moderated mediation analysis were performed to understand how data visualization affects factors such as attitude, or interest, and affects public comprehension. The results showed significant indirect effects that indicate that reading a data visualization news item increases comprehension and, with it, positive attitudes toward data visualization. Variables related to comprehension and interest have been found to have a significant impact on attitudes toward data viewing, opening new lines of research to delve into the factors that affect data-driven news performance.
Dynamic Weights Based Risk Rule Generation Algorithm for Incremental Data of Customs Declarations

Aimed at shortcomings, such as fewer risk rules for assisting decision-making in customs entry inspection scenarios and relying on expert experience generation, a dynamic weight assignment method based on the attributes of customs declaration data and an improved dynamic-weight Can-Tree incremental mining algorithm are proposed. In this paper, we first discretize the customs declaration data, and then form composite attributes by combining and expanding the attributes, which is conducive to generating rules with risk judgment significance. Then, weights are determined according to the characteristics and freshness of the customs declaration data, and the weighting method is applied to the Can-Tree algorithm for incremental association rule mining to automatically and efficiently generate risk rules. By comparing FP-Growth and traditional Can-Tree algorithms experimentally, the improved dynamic-weight Can-Tree incremental mining algorithm occupies less memory space and is more time efficient. The introduction of dynamic weights can visually distinguish the importance level of customs declaration data and mine more representative rules. The dynamic weights combine confidence and elevation to further improve the accuracy and positive correlation of the generated rules.
On a Discriminator for the Polynomial $$f(x)=x^3+x$$

Let $$\Delta (n)$$ denote the smallest positive integer m such that $$a^3+a(1\leqslant a\leqslant n)$$ are pairwise distinct modulo m. The purpose of this paper is to determine $$\Delta (n)$$ for all positive integers n.
On the readability of texts presented in sentence-by-sentence segments to beginner readers: Evidence from self-paced reading and eye tracking

Abstract Many digital reading applications have built-in features to control the presentation flow of texts by segmenting those texts into smaller linguistic units. Whether and how these segmentation techniques affect the readability of texts is largely unknown. With this background, the current study examined a recent proposal that a sentence-by-sentence presentation mode of texts improves reading comprehension of beginning readers because this presentation mode encourages them to engage in more effortful sentence wrap-up processing. In a series of self-paced reading and eye-tracking experiments with primary school pupils as participants (6–9 years old; n = 134), reading speed and text comprehension were assessed in a full-page control condition—i.e., texts were presented in their entirety—and in an experimental condition in which texts were presented in sentence-by-sentence segments. The results showed that text comprehension scores were higher for segmented texts than for full-page texts. Furthermore, in the final word-regions of the sentences in the texts, the segmented layout induced longer reading times than the full-page layout did. However, mediation analyses revealed that these inflated reading times had no, or even a disruptive influence on text comprehension. This indicates that the observed comprehension advantage for segmented texts cannot be attributed to more effortful sentence wrap-up. A more general implication of these findings is that the segmentation features of reading applications should be used with caution (e.g., in educational or professional settings) because it is unclear how they affect the perceptual and cognitive mechanisms that underlie reading.
A decentralized crypto network with dynamic threshold change

Abstract The threshold proxy re‐encryption (tPRE) algorithm not only ensures the separation of data ownership and use right, but also prevents a single data proxy from doing evil. In order to avoid collusion of multiple data proxies, this paper constructs a decentralized crypto network (DCN) based on blockchain technology and tPRE. DCN is more than a key management system, while expanding the data storage capacity of blockchain network. Combined with existing distributed storage schemes, it can store massive data. Due to Byzantine failure in distributed networks, the data proxy nodes can join or leave at will. In order to ensure that the number of data proxy nodes in the DCN network meets the requirements of the re‐encryption threshold, and the data authorization operation can be carried out smoothly, this paper presents a dynamic refresh method of tPRE fragments. When there are fragments that meet the threshold number in the network, the allocation of encrypted fragments can be adjusted non‐interactivity and self‐adaptively. Non‐interactivity means that each data proxy node independently refreshes its old fragments into new fragments without collecting old fragments from other nodes in the DCN, and then distributes the new fragments to other data proxy nodes in the DCN. Self‐adaptability refers to monitoring the changes of data proxy node sets in the DCN, and automatically refreshing fragments. Finally, the performance of critical algorithms is calculated in the proposal.
An Automatic Recognition System for Digital Collections of Indonesian Traditional Houses Using Convolutional Neural Networks for Cultural Heritage Preservation

Indonesia is one of the archipelago countries located in Asia and it has diverse cultures. In modern society, Indonesian traditional houses have become rare and need to be preserved. This research is conducted to build a digital collection and to develop an image-based automatic recognition system for Indonesian traditional houses. In this paper, the traditional house images are collected in several ways: on-site image captures, receiving images from volunteers, and collecting public images from Google. The dataset is limited to the collection of building shape images, excluding the interior design. The authors implement Convolutional Neural Networks (ConvNets) to build a model for an automatic recognition system. The experiments run some deep network models: VGG, DenseNet, Inception, Xception, MobileNetV2, NasNetMobile, and EfficientNet. The experiments involve 1526 images of 16 classes. EfficientNet-Lite0 outperforms other models and produces the average F1-score and accuracy of 90.1% and 91.8%, respectively. ConvNets also outperform conventional classifiers.
The State Scientific Technical and Economic Library of the RSFSR Supreme Council of National Economy Sci-tech Department: The beginnings (1918–1919)

The author reviews the history of Russian central sci-tech library in 1918-1919. During this period, the library was called The State Scientific Technical and Economic Library (SSTEL) and was part of the Sci-tech Department of the Supreme Council of National Economy of the Russian Soviet Federative Socialist Republic. Further, in 1927-1958, this largest library organization was called The State Scientific Library. It became the foundation to the Russian National Public Library for Science and Technology and State Public Scientific &amp; Technological Library of the Russian Academy of Sciences Siberian Branch. The author analyzes the pool of the government and departmental solutions that laid the basis for the Library; he also describes organization process, and reviews personal contributions of librarians to the initial library organization and collection development. He characterizes the first SSTEL leaders and librarians (M.L. Milshtein, Karl F. Gessel, Nikolay N. Orlov). In particular, he emphasizes the outstanding contribution by Nikolay P. Gorbunov (former assistant to Vladimir Lenin, later an academic). At the initial stages, two well-stocked public libraries were merged into SSTEL, namely the Society for Advancement of Experimental Sciences and Their Practical Applications named by H.S. Ledentsov (and its specialized library building) and Moscow Technical Library of the Polytechnic Society. The author concludes that the efforts of librarians and scholars in the Supreme Council of National Economy made it possible to establish the central national sci-tech library and get it off the ground during the hardest years of the Civil War.
Efficient state representation with artificial potential fields for reinforcement learning

Abstract In the complex tasks environment, efficient state feature learning is a key factor to improve the performance of the agent’s policy. When encountering a similar new environment, reinforcement learning agents usually need to learn from scratch. However, humans naturally have a common sense of the environment and are able to use prior knowledge to extract environmental state features. Although the prior knowledge may not be fully applicable to the new environment, it is able to speed up the learning process of the state feature. Taking this inspiration, we propose an artificial potential field-based reinforcement learning (APF-RL) method. The method consists of an artificial potential field state feature abstractor (APF-SA) and an artificial potential field intrinsic reward model (APF-IR). The APF-SA can introduce human knowledge to accelerate the learning process of the state feature. The APF-IR can generate an intrinsic reward to reduce the invalid exploration and guide the learning of the agent’s policy. We conduct experiments on PySC2 with different mini-games. The experimental results show that the APF-RL method achieves improvement in the learning efficiency compared to the benchmarks.
GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification

In recent years, graph neural networks (GNNs) have played an important role in graph representation learning and have successfully achieved excellent results in semi-supervised classification. However, these GNNs often neglect the global smoothing of the graph because the global smoothing of the graph is incompatible with node classification. Specifically, a cluster of nodes in the graph often has a small number of other classes of nodes. To address this issue, we propose a graph-retraining neural network (GRNN) model that performs smoothing over the graph by alternating between a learning procedure and an inference procedure, based on the key idea of the expectation-maximum algorithm. Moreover, the global smoothing error is combined with the cross-entropy error to form the loss function of GRNN, which effectively solves the problem. The experiments show that GRNN achieves high accuracy in the standard citation network datasets, including Cora, Citeseer, and PubMed, which proves the effectiveness of GRNN in semi-supervised node classification.
Research on electrical load distribution using an improved bacterial foraging algorithm

This paper proposes an improved bacterial foraging algorithm for electrical load distribution to impro-ve power plants’ efficiency and reduce energy consumption costs. In the chemotaxis stage, the adaptive step size is introduced to accelerate the random search speed compared with the traditional algorithm. In the replication stage, a hybrid crisscross operator is proposed to replace the traditional binary replication method in the algorithm to ensure the diversity of the population and improve the efficiency of the algorithm. The adaptive dynamic probability is used instead of the initial fixed probability to improve the global search performance of the algorithm. The mathematical model of electrical load distribution in a natural power plant is established, and the improved bacterial foraging algorithm is used to solve the model. Through comparative analysis of two power plant unit experiments, it is proved that the results of the improved algorithm can reduce 3.671% and 1.06% respectively compared with the particle swarm optimization algorithm, and 7.26% and 1.37% respectively compared with the traditional bacterial foraging algorithm, which can significantly reduce the coal consumption of the power plant.
Improved statistical fluctuation analysis for two decoy-states phase-matching quantum key distribution

Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution, breaking through the traditional linear key-rate bound. In practical applications, finite data size can cause significant system performance to deteriorate when data size is below 10 10 . In this work, an improved statistical fluctuation analysis method is applied for the first time to two decoy-states phase-matching quantum key distribution, offering a new insight and potential solutions for improving the key generation rate and the maximum transmission distance while maintaining security. Moreover, we also compare the influence of the proposed improved statistical fluctuation analysis method on system performance with those of the Gaussian approximation and Chernoff–Hoeffding boundary methods on system performance. The simulation results show that the proposed scheme significantly improves the key generation rate and maximum transmission distance in comparison with the Chernoff–Hoeffding approach, and approach the results obtained when the Gaussian approximation is employed. At the same time, the proposed scheme retains the same security level as the Chernoff–Hoeffding method, and is even more secure than the Gaussian approximation.
Sustainable and Cost-Effective Gel Documentation

A common laboratory method involves gel electrophoresis followed by photographic documentation of the results, a procedure which is performed worldwide by students and experienced scientists alike. Proprietary Gel Documentation Systems are convenient and useful for documentation of electrophoresis results, but the systems can be prohibitively expensive to purchase and repair, they contain features that are not necessary for everyday documentation, and some users may not find the systems intuitive to operate. We describe our gel documentation setup that meets the everyday needs for documentation in our lab. The setup is inexpensive, modular, user friendly, and increases sustainability through extending the working life of obsolete cell phones, iPads, or other electronic devices containing a camera. More importantly, the setup completely shields users from potentially damaging ultraviolet radiation.