Reviewerly/exHarmony huggingface dataset:
On completely factoring any integer efficiently in a single run of an order-finding algorithm

Abstract We show that given the order of a single element selected uniformly at random from $${\mathbb {Z}}_N^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>Z</mml:mi> <mml:mi>N</mml:mi> <mml:mo>∗</mml:mo> </mml:msubsup> </mml:math> , we can with very high probability, and for any integer N , efficiently find the complete factorization of N in polynomial time. This implies that a single run of the quantum part of Shor’s factoring algorithm is usually sufficient. All prime factors of N can then be recovered with negligible computational cost in a classical post-processing step. The classical algorithm required for this step is essentially due to Miller.
HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text

We present a modern hybrid paradigm for managing tacit semantic awareness and qualitative meaning in short texts. The main goals of this proposed technique are to use deep learning approaches to identify multilevel textual sentiment with far less time and more accurate and simple network structure training for better performance. In this analysis, the proposed new hybrid deep learning HARC model architecture for the recognition of multilevel textual sentiment that combines hierarchical attention with Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM) outperforms other compared approaches. BiGRU and BiLSTM were used in this model to eliminate individual context functions and to adequately manage long-range features. Dilated CNN was used to replicate the retrieved feature by forwarding vector instances for better support in the hierarchical attention layer, and it was used to eliminate better text information using higher coupling correlations. Our method handles the most important features to recover the limitations of handling context and semantics sufficiently. On a variety of datasets, our proposed HARC algorithm solution outperformed traditional machine learning approaches as well as comparable deep learning models by a margin of 1%. The accuracy of the proposed HARC method was 82.50 percent IMDB, 98.00 percent for toxic data, 92.31 percent for Cornflower, and 94.60 percent for Emotion recognition data. Our method works better than other basic and CNN and RNN based hybrid models. In the future, we will work for more levels of text emotions from long and more complex text.
Intelligent Mathematics (IM): Indispensable Mathematical Means for General AI, Autonomous Systems, Deep Knowledge Learning, Cognitive Robots, and Intelligence Science

It is recognized that the core knowledge of humans in natural sciences and engineering is archived in mathematical forms. Intelligent Mathematics (IM) is a category of contemporary denotational mathematics extending classic analytic mathematics as defined in real numbers (R). IM represents a collection of novel mathematical structures that formalizes rigorous expressions and manipulations on complex entities known as hyperstructures (H) beyond R. Instances of the complex entities in H include formal concepts, semantics, relations, knowledge, intelligence, behavioral processes, causality, inferences, and systems. Paradigms of IM developed in my lab include real-time process algebra (RTPA), concept algebra, semantic algebra, system algebra, inference algebra, fuzzy probability algebra, big data algebra, image frame algebra, and the causal probability theory, etc.This keynote speech presents the IM foundations of emerging intelligent science and AI paradigms. A set of novel IMs will be presented for rigorously manipulating complex cognitive entities in the brain and abstract intelligence including data, information, knowledge, and intelligence from the bottom up. IM will lead to the emergence of mathematical engineering (ME), which addresses the challenges in formal structural and functional modeling of complex mental objects and their rigorous manipulations in a wide range of applications such as cognitive robots, autonomous systems, intelligent IoT, and unmanned systems.
Improvement of Vision-based Drone Detection and Tracking by Removing Cluttered Background, Shadow and Water Reflection with Super Resolution

Detection and tracking of small and fast moving aerial targets especially drones has got attention nowadays. This paper focuses on the vision-based technique using images taken from observing cameras. In real life situations, the target drone objects maybe are hidden in the cluttered background such as trees shadow or foliage, rows of buildings, and other kind of scenery that will hinder the clear indication of the drones. The object is further confused by the presence of its shadow and reflection from the water or glass wall reflection. For vision-based objection, the clarity and the ambiguity of the target images in the video stream are the key for effective and successful target detection and tracking. Here, we present the improvement by mitigating the effect of cluttered background, shadow and water reflection to the target images. We applied the schemes to make the drone more visible and more clear. We also implemented super resolution to increase the image resolution for more precise detection and tracking. As a result, the target drone could be detected and tracked throughout the sample clips. The comparative tracking results using DCF are presented. Likewise, we applied the water removal scheme to eliminate the reflection to avoid confusion to the tracker. With this, just the correct drone targets were detected and tracked. Overall, the drones could be detected and tracked all the way, as long as they appeared in the camera scene.
Robust supervised topic models under label noise

Recently, some statistical topic modeling approaches have been widely applied in the field of supervised document classification. However, there are few researches on these approaches under label noise, which widely exists in real-world applications. For example, many large-scale datasets are collected from websites or annotated by varying quality human-workers, and then have a few mislabeled items. In this paper, we propose two robust topic models for document classification problems: Smoothed Labeled LDA (SL-LDA) and Adaptive Labeled LDA (AL-LDA). SL-LDA is an extension of Labeled LDA (L-LDA), which is a classical supervised topic model. The proposed model overcomes the shortcoming of L-LDA, i.e., overfitting on noisy labels, through Dirichlet smoothing. AL-LDA is an iterative optimization framework based on SL-LDA. At each iterative procedure, we update the Dirichlet prior, which incorporates the observed labels, by a concise algorithm based on maximizing entropy and minimizing cross-entropy principles. This method avoids identifying the noisy label, which is a common difficulty existing in label noise cleaning algorithms. Quantitative experimental results on noisy completely at random (NCAR) and Multiple Noisy Sources (MNS) settings demonstrate our models have outstanding performance under noisy labels. Specially, the proposed AL-LDA has significant advantages relative to the state-of-the-art topic modeling approaches under massive label noise.
Penerapan Pendidikan Karakter Gemar Membaca Melalui Program Literasi di Sekolah Dasar

This study aims to find a model for the application of character education for reading fondness through literacy programs. This research is a type of qualitative research with a case study approach. The research subjects consisted of the principal, teachers, and students at SD Negeri Sidareja 01 Cilacap. In collecting data, researchers used instruments in the form of interview guidelines, observation guidelines, and document analysis guidelines. The data analysis technique used is the interactive data analysis technique of Miles and Huberman which consists of data reduction, data presentation, and conclusion drawing. The results showed that character education for reading fondness through the School Literacy Program at SD Negeri Sidareja 01 was carried out through: 1) The habituation phase which includes reading habit for 10-15 minutes and other activities that can build literacy culture and literacy-friendly physical environment conditioning. 2) The development phase which includes the development of literacy skills through various non-academic activities as well as the promotion of the social and affective environment as a model of literate communication and interaction. 3) The learning phase which includes the implementation of learning using a variety of literacy strategies and making schools a literate academic environment through professional development.
Out-of-Distribution Generalization via Risk Extrapolation (REx)

Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that variation across training domains is representative of the variation we might encounter at test time, but also that shifts at test time may be more extreme in magnitude. In particular, we show that reducing differences in risk across training domains can reduce a model’s sensitivity to a wide range of extreme distributional shifts, including the challenging setting where the input contains both causal and anti-causal elements. We motivate this approach, Risk Extrapolation (REx), as a form of robust optimization over a perturbation set of extrapolated domains (MM-REx), and propose a penalty on the variance of training risks (V-REx) as a simpler variant. We prove that variants of REx can recover the causal mechanisms of the targets, while also providing some robustness to changes in the input distribution (``covariate shift''). By appropriately trading-off robustness to causally induced distributional shifts and covariate shift, REx is able to outperform alternative methods such as Invariant Risk Minimization in situations where these types of shift co-occur.
Exploring Information Systems Curricula

Abstract The study considers the application of text mining techniques to the analysis of curricula for study programs offered by institutions of higher education. It presents a novel procedure for efficient and scalable quantitative content analysis of module handbooks using topic modeling. The proposed approach allows for collecting, analyzing, evaluating, and comparing curricula from arbitrary academic disciplines as a partially automated, scalable alternative to qualitative content analysis, which is traditionally conducted manually. The procedure is illustrated by the example of IS study programs in Germany, based on a data set of more than 90 programs and 3700 distinct modules. The contributions made by the study address the needs of several different stakeholders and provide insights into the differences and similarities among the study programs examined. For example, the results may aid academic management in updating the IS curricula and can be incorporated into the curricular design process. With regard to employers, the results provide insights into the fulfillment of their employee skill expectations by various universities and degrees. Prospective students can incorporate the results into their decision concerning where and what to study, while university sponsors can utilize the results in their grant processes.
Learning to Decompose and Organize Complex Tasks

People rely on digital task management tools, such as email or to-do apps, to manage their tasks.Some of these tasks are large and complex, leading to action paralysis and feelings of being overwhelmed on the part of the user.The micro-productivity literature has shown that such tasks could benefit from being decomposed and organized, in order to reduce user cognitive load.Thus in this paper, we propose a novel end-to-end pipeline that consumes a complex task and induces a dependency graph from unstructured text to represent sub-tasks and their relationships.Our solution first finds nodes for sub-tasks from multiple 'how-to' articles on the web by injecting a neural text generator with three key desiderata -relevance, abstraction, and consensus.Then we resolve and infer edges between these subtask nodes by learning task dependency relations.We collect a new dataset of complex tasks with their sub-task graph to develop and evaluate our solutions.Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.Our generalizable and scalable endto-end solution has important implications for boosting user productivity and assisting with digital task management.
A New Vulnerability Reporting Framework for Software Vulnerability Databases

Cyber security is one of the fundamental research areas of software engineering.The systems that make up today's information systems infrastructure have been developed largely with software support.Security vulnerabilities in the software used in these systems may cause undesirable results.It is very important to manage software vulnerabilities correctly.In addition, an effective communication mechanism and certain standards should be established among those working in this field.The importance of the subject has been understood in recent years and the studies in this area have gradually increased.The use of machine learning algorithms is increasing in recent studies in this area.Although there is a large data set accumulated in vulnerability databases, there is often the problem of unstructured data.Vulnerability databases and security reports are created in natural language that people can understand and interpret.These reports are difficult to read and understand by machines.Our study focuses on the difficulties of this unstructured and natural language system.In order to investigate this problem, firstly, up-to-date and accessible databases used in scientific research were examined and evaluated.Then, a three-stage security framework was proposed, consisting of the use of vulnerabilities by machines to assist experts from the notification stage to the reporting stage.The rules and flow charts of each stage are defined.In order to increase the usability of different databases in their own systems, the framework rules are defined as a guideline containing flexible directions, not rigid items.The point of consideration is not the methods and tools used, but the definition of outputs as common and similar attributes.
Prioritized Level Replay

Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset of levels allows for testing generalization to unseen levels. What can be learned from a level depends on the current policy, yet prior work defaults to uniform sampling of training levels independently of the policy. We introduce Prioritized Level Replay (PLR), a general framework for selectively sampling the next training level by prioritizing those with higher estimated learning potential when revisited in the future. We show TD-errors effectively estimate a level's future learning potential and, when used to guide the sampling procedure, induce an emergent curriculum of increasingly difficult levels. By adapting the sampling of training levels, PLR significantly improves sample efficiency and generalization on Procgen Benchmark--matching the previous state-of-the-art in test return--and readily combines with other methods. Combined with the previous leading method, PLR raises the state-of-the-art to over 76% improvement in test return relative to standard RL baselines.
Categorization in Unsupervised Generative Selflearning Systems

In this study the authors investigated the connections between the training processes of unsupervised neural network models with self-encoding and regeneration and the information structure in the representations created by such models.We propose theoretical arguments leading to conclusions, confirmed by previously published experimental results that unsupervised representations obtained under certain constraints in training compliant with Bayesian inference principle, favor configurations with better categorization of hidden concepts in the observable data.The results provide an important connection between training of unsupervised machine learning models and the structure of representations created by them and can be used in developing new methods and approaches in self-learning as well as provide insights into common principles underlying the emergence of intelligence in machine and biologic systems.
Neural Machine Translation without Embeddings

Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms.A simple universal alternative is to represent every computerized text as a sequence of bytes via UTF-8, obviating the need for an embedding layer since there are fewer token types (256) than dimensions.Surprisingly, replacing the ubiquitous embedding layer with one-hot representations of each byte does not hurt performance; experiments on byte-to-byte machine translation from English to 10 different languages show a consistent improvement in BLEU, rivaling character-level and even standard subwordlevel models.A deeper investigation reveals that the combination of embeddingless models with decoder-input dropout amounts to token dropout, which benefits byte-to-byte models in particular. 1
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization

In this paper, we describe our approach to question summarization and multi-answer summarization in the context of the 2021 MEDIQA shared task (Ben Abacha et al., 2021).We propose two kinds of transfer learning for the abstractive summarization of medical questions.First, we train on Health-CareMagic, a large question summarization dataset collected from an online healthcare service platform.Second, we leverage the ability of the BART encoder-decoder architecture to model both generation and classification tasks to train on the task of Recognizing Question Entailment (RQE) in the medical domain.We show that both transfer learning methods combined achieve the highest ROUGE scores.Finally, we cast the question-driven extractive summarization of multiple relevant answer documents as an Answer Sentence Selection (AS2) problem.We show how we can preprocess the MEDIQA-AnS dataset such that it can be trained in an AS2 setting.Our AS2 model is able to generate extractive summaries achieving high ROUGE scores.
Research on Real-Time Reasoning Based on JetSon TX2 Heterogeneous Acceleration YOLOv4

Aiming at the problem of high CPU resource occupancy rate and large delay on the embedded side when the YOLOv4 target detection algorithm is in model inference. This research proposes an embedded platform based on JetSon TX2, using CPU+GPU heterogeneous mode, deploying a YOLOv4 network model improved by the K-means clustering algorithm, and real-time detection of infrared long-wave images containing 10 types of military targets. Using the advantages of CPU multicore serial, load the weight file of the YOLOv4 model and the cache of the input matrix; GPU core CUDA core accelerates the convolution and clustering of the YOLOv4 model. The experimental results show that two independent experiments of CPU, CPU+GPU on Jetson TX2 platform, the Mean Average Precision of YOLOv4 model inference is 93%; the delay of detecting a single long-wave infrared image is 21267.9ms and 402.4ms respectively; When the target in the video stream, the frame rate of the CPU is less than 0.1FPS, and the frame rate of the GPU can reach 3.2FPS; the average occupancy rate of multi-core CPU resources is 96.3% and 45.0% respectively. It can be seen that when the mAP of the detection target is 93%, the resource consumption and delay of the CPU+GPU heterogeneous mode are better than the CPU architecture, which can provide a reference for the subsequent real-time detection of military targets in the actual combat environment.
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition

Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types.However, the NER performance is still moderate for specialized domains that tend to feature complicated contexts and jargonistic entity types.To address these challenges, we propose explicitly connecting entity mentions based on both global coreference relations and local dependency relations for building better entity mention representations.In our experiments, we incorporate entity mention relations by Graph Neural Networks and show that our system noticeably improves the NER performance on two datasets from different domains.We further show that the proposed lightweight system can effectively elevate the NER performance to a higher level even when only a tiny amount of labeled data is available, which is desirable for domain-specific NER. 1
Towards an Ecosystem of Domain Specific Languages for Threat Modeling

Today, many of our activities depend on the normal operation of the IT infrastructures that supports them. However, cyber-attacks on these infrastructures can lead to disastrous consequences. Therefore, efforts towards assessing the cyber-security are being done, such as attack graph simulations based on system architecture models. The Meta Attack Language (MAL) was previously proposed as a framework for developing Domain Specific Languages (DSLs) that can be used for the aforementioned purpose. Since many common components exist among different domains, a way to prevent repeating work had to be defined. To facilitate this goal, we adapt taxonomy building by Nickerson and propose an ecosystem of MAL-based DSLs that describes a systematic approach for not only developing, but also maintaining them over time. This can foster the usage of MAL for modeling new domains.
Optimal Quantum Spatial Search with One-Dimensional Long-Range Interactions

Continuous-time quantum walks can be used to solve the spatial search problem, which is an essential component for many quantum algorithms that run quadratically faster than their classical counterpart, in $\mathcal{O}(\sqrt{n})$ time for $n$ entries. However, the capability of models found in nature is largely unexplored---e.g., in one dimension only nearest-neighbor Hamiltonians have been considered so far, for which the quadratic speedup does not exist. Here, we prove that optimal spatial search, namely with $\mathcal{O}(\sqrt{n})$ run time and high fidelity, is possible in one-dimensional spin chains with long-range interactions that decay as $1/{r}^{\ensuremath{\alpha}}$ with distance $r$. In particular, near unit fidelity is achieved for $\ensuremath{\alpha}\ensuremath{\approx}1$ and, in the limit $n\ensuremath{\rightarrow}\ensuremath{\infty}$, we find a continuous transition from a region where optimal spatial search does exist ($\ensuremath{\alpha}&lt;1.5$) to where it does not ($\ensuremath{\alpha}&gt;1.5$). Numerically, we show that spatial search is robust to dephasing noise and that, for reasonable chain lengths, $\ensuremath{\alpha}\ensuremath{\lesssim}1.2$ should be sufficient to demonstrate optimal spatial search experimentally with near unit fidelity.