Reviewerly/exHarmony huggingface dataset:
Limitations of Mean-Based Algorithms for Trace Reconstruction at Small Edit Distance

Trace reconstruction considers the task of recovering an unknown string <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}\in \{0,1\}^{n}$ </tex-math></inline-formula> given a number of independent “traces”, i.e., subsequences of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}$ </tex-math></inline-formula> obtained by randomly and independently deleting every symbol of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}$ </tex-math></inline-formula> with some probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> . The information-theoretic limit of the number of traces needed to recover a string of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is still unknown. This limit is essentially the same as the number of traces needed to determine, given strings <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {y}$ </tex-math></inline-formula> and traces of one of them, which string is the source. The most-studied class of algorithms for the worst-case version of the problem are “mean-based” algorithms. These are a restricted class of distinguishers that only use the mean value of each coordinate on the given samples. In this work we study limitations of mean-based algorithms on strings at small Hamming or edit distance. We show that, on the one hand, distinguishing strings that are nearby in Hamming distance is “easy” for such distinguishers. On the other hand, we show that distinguishing strings that are nearby in edit distance is “hard” for mean-based algorithms. Along the way, we also describe a connection to the famous Prouhet-Tarry-Escott (PTE) problem, which shows a barrier to finding explicit hard-to-distinguish strings: namely such strings would imply explicit short solutions to the PTE problem, a well-known difficult problem in number theory. Furthermore, we show that the converse is also true, thus, finding explicit solutions to the PTE problem is equivalent to the problem of finding explicit strings that are hard-to-distinguish by mean-based algorithms. Our techniques rely on complex analysis arguments that involve careful trigonometric estimates, and algebraic techniques that include applications of Descartes’ rule of signs for polynomials over the reals.
Study of Experience in Blended Learning During the Pandemic: Comparative Analysis of Russian Universities

The goal of the article is studying of the preferable combinations of learning formats under pandemic risks based on experience of blended learning accumulated in the year 2020 and comparative analysis of universities in Murmansk region and St. Petersburg. The utmost need in adaptation of modern universities to different types of risks to ensure sustainable educational process defines the topicality of the conducted study. The methodological basis of the research is interviewing methods and techniques. The survey developed by the authors allows to discover respondents’ preferences and perceptions regarding the most convenient combinations of the learning formats under different level of risk. The survey consists of the three chapters excluding biographic data and 11 questions in total. The analysis of the interviewing results reveals the main factors of influencing of students’ perceptions towards the most convenient combination of learning formats. The policy pursued by the universities, as well as established practices aimed at building the skills for using interactive technologies in the learning process and the prevailing living conditions in the regions are among these factors. Thus, the study confirmed that students’ attitudes towards the preferred format depend on the given region. The authors distinguish the study limitations and main directions of the future research.
User Experience and Music Perception in Broadcasts: Sensory Input Classification

Mobile devices of any sort, size and construction have increased drastically the sensory input of the Internet in unprecedented levels. Massive recordings of every day artistic activities, in the form of sound archives, photographs, movie films footages or video signals have proved to be particularly successful and influential within the social services’ immense cloud. Mobile devices prove to be the most convenient means to input in digital format what millions of correspondents notice with their physical senses happening in their everyday cognitive walk within their natural habitat. As a result, the amount of sensory input, within each user’s walkthrough in his digital habitat, has increased drastically for hearings that have been not that much promoted by the established media hubs - which are more or less using digital footages highly influenced by the US movie industry and the lifestyles of people within each country that are behaving accordingly to the prevalent model.
A new measurement method of Chinese texts’ difficulty based on the digital analysis of two-character continuations

Abstract A new measurement method of Chinese texts’ difficulty based on the digital analysis of two-character continuations is proposed, which is ‘i + 1’ measurement model of language difficulty. The new method shifts the focus of the measurement of text difficulty from language units and grammatical complexity to combinations of language units, which breaks the limitations of existent methods of the measurement of Chinese texts’ difficulty. First, the effectiveness of the two-character continuations in the differentiation of texts difficulties is verified. Second, for the application of the ‘i + 1’ model in measuring text difficulty, the pedagogy and language acquisition theories are analyzed to check its rationality. Third, a corpus of two-character continuations is built up, with which the empirical values of ‘1’ for two-character continuations with various difficulty levels in the ‘i + 1’ measurement model are obtained. Subsequently, the testing texts, the two-character continuations corpus, and the empirical values of ‘1’ are integrated to test the validity of the ‘i + 1’ model in the measurement of text difficulty. The research shows that, compared to those methods based on the complex features of language, the ‘i + 1’ measurement model based on two-character continuations, with fewer indicators and more convenience in the measurement, demonstrates better measurement result of text difficulty.
Evolution of Acoustic Logic Gates in Granular Metamaterials

Granular metamaterials are a promising choice for the realization of mechanical computing devices. As preliminary evidence of this, we demonstrate here how to embed Boolean logic gates (AND and XOR) into a granular metamaterial by evolving where particular grains are placed in the material. Our results confirm the existence of gradients of increasing "AND-ness" and "XOR-ness" within the space of possible materials that can be followed by evolutionary search. We measure the computational functionality of a material by probing how it transforms bits encoded as vibrations with zero or non-zero amplitude. We compared the evolution of materials built from mass-contrasting particles and materials built from stiffness-contrasting particles, and found that the latter were more evolvable. We believe this work may pave the way toward evolutionary design of increasingly sophisticated, programmable, and computationally dense metamaterials with certain advantages over more traditional computational substrates.
Bracing for the Super-Aged Society: A New Era for Active Aging Learning

Since 1993, Taiwan has officially been characterized as an ageing society by the World Health Organization due to the rapid increase in its ageing population. In 2008, the number of individuals over the age of 65 had reached 14% of the entire population and Taiwan has become an aged society. In response to a steeply rising ageing population, the Taiwan Ministry of Education has begun an Active Aging Learning initiative which entailed implementing a significant number of educational programs in various communities and colleges throughout Taiwan. This not only revolutionized the traditional approach aimed at promoting life-long learning for senior citizens but also brought a new way of thinking about the older population and the second half of life.
An Approach Based on Semantic Similarity to Explaining Link Predictions on Knowledge Graphs

We propose approxSemanticCrossE, an approach for generating explanations to link prediction problems on Knowledge Graphs. Due to their incompleteness, several models have been proposed to predict missing relationships (link prediction task). To date, the most effective methods are based on embedding models, representing entities and relationships as a multi-dimensional vectors in a vector space. Explaining the results of this task means finding a meaningful reason for which entities are predicted as linked. This work presents a structural and semantically enriched approach for generating explanations for link predictions, by exploring the data available in the knowledge graph. The solution searches for paths and examples of similar situations that justify the prediction carried out using numerical approaches. Specifically, CrossE is adopted as the underlying embedding model to compute predictions. Then explanations are searched exploiting ad hoc semantic similarity measures. The proposed solution has been experimentally evaluated, showing that the new approach is able to provide meaningful explanations compared to the considered baseline.
Blockchain meets healthcare: Architecture for secure data sharing in unobtrusive medical applications

Modernages have perceived an exemplar swing in warehousing Electronic Health Records (EHRs) on cloud settings with the catholic solicitation of mobile healthcare systems, where mobile devices are assimilated by means of cloud computing to assistmedicinal data interactions amid patients and surgeons. The distribution of EHRs has prodigious optimistic connotation for investigation of disease and doctors' diagnosis. This radical model empowers healthcare amenities with truncatedoperating charge, high flexibility, as well as EHRs accessibility. It is of paramount significance to hitch therapeutic evidence sprinkled through healthcare establishments to provision exhaustive data investigation and accomplishcustom-made healthcare. Nevertheless, this newfangledarchetype also nurtures apprehensions about data privacy in addition to network security for healthcare schemes. Exactly how to stead fastly stake data among mobile consumers while pledging sophisticated security echelons in cloud is a perplexing concern. Blockchain, by means of a public ledger pigeon holed through its transparency, tamper-evidence, decentralization, unforgeability, verifiability, as well as anonymity can supportin forming a secure healthcare scheme. Existing works primarily recompensed evotion to centralized in addition toblockchain-based mechanisms. Nonetheless they could not acclimatize to the snow balling volume of global medical records and agonize from multifaceted application encounters, singly. In this paper, we recommend a Diagonal Digital Signature Algorithm (DDSA) with Merkle Patricia Hash Trie (MPHT) algorithm which is aninnovative EHRs sharing framework that exploitsblockchain to expedite secure medical record sharing amiddissimilar patients and surgeons. The pragmaticoutcomesillustrate that our schemeaffords an operativeelucidation for unswerving data interactions on cloud while conserving delicate medical records in contrast to impending intimidations. The scheme assessment along with the security investigation also exhibit the performance enhancements in light-weight access control; slightest network latency, transmission delay and file loss rate; with extraordinary security as well as data privacy, paralleled to the prevailing record sharing schemes.
A Phenetic Approach to Selected Variants of Arabic and Aramaic Scripts

This paper aims to introduce the phenetic method for processing paleographical datasets and evaluating their similarity relationships. The presented numerical taxonomic method was applied for selected varieties of the Arabic and Aramaic scripts. The phenetic model was evaluated by hierarchical clustering and—after applying multidimensional scaling—a centroid-based clustering method. The hierarchical clustering results were presented as dendrograms (phenograms), while the centroid-based results were given in 2- and 3-dimensional Cartesian coordinate systems. The obtained results demonstrate that the numerical taxonomy's phenetic approach is useful in describing the distances between different writing systems. The long-term goal of the research is to apply machine learning tools to clarify the relationships between the large number of Aramaic and Arabic script variants. This study belongs to the field of pattern evolution, in which machine learning methods of biological evolution are used to model evolving patterns (such as writing systems) over time.
Review on the Entity Extraction Methods for Low-resource Languages

With the economy developing as one of the key technologies of the construction knowledge graph, entity extraction has attracted more and more researchers' attention. To understand the latest developments, this paper introduced the emergence and development of entity extraction firstly; Then, the related methods are systematically sorted out and summarized. Finally, explained the current improved methods of entity extraction that are researched in low- resource language in natural language processing.
An Interactive Machine Translation Framework for Modernizing the Language of Historical Documents

In order to make historical documents accessible to a broader audience, language modernization generates a new version of a given document in the modern version of its original language. However, while they succeed in their goal, these modernizations are far from perfect. Thus, in order to help scholars generate error-free modernizations when the quality is essential, we propose an interactive language modernization framework based on interactive machine translation. In this work, we deployed two successful interactive protocols into language modernization. We evaluated our proposal on a simulated environment observing significant reductions of the human effort.
Reviewer Recommendations Using Document Vector Embeddings and a Publisher Database: Implementation and Evaluation

We develop and evaluate an automated data-driven framework for providing
A Label Extension Schema for Improved Text Emotion Classification

Due to the subjectiveness and fuzziness of emotions in texts, researchers have been aware that it is ubiquitous to observe multiple emotions in a sentence, and the one-hot label approach is not informative enough in emotion-relevant text classification tasks. Therefore, to facilitate the classification task, recent works focus on generating and employing a coarse-grained emotion distribution, which is based on coarse-grained labels provided by the underlying dataset. Although such methods can alleviate the problem of overfitting and improve robustness, they may cause inter-class confusion between similar emotion categories and introduce undesirable noise during training. Meanwhile, current studies neglect the fine-grained emotions associated with these coarse-grained labels. To address the issue caused by utilizing a coarse-grained distribution, we propose in this paper a general and novel emotion label extension method based on fine-grained emotions. Specifically, we first identify a mapping function between coarse-grained emotions and fine-grained emotion concepts, and extend the original label space with specific fine-grained emotions. Then, we generate a fine-grained emotion distribution by employing a rule-based method, and utilize it as a model constraint to incorporate the dependencies among fine-grained emotions to predict the original coarse-grained emotion labels. We conduct extensive experiments to demonstrate the effectiveness of our proposed label extension method. The results indicate that our proposed method can produce notable improvements over baseline models on the applied datasets.
Efficient Keyword Spotting by Capturing Long-Range Interactions with Temporal Lambda Networks

Models based on attention mechanisms have shown unprecedented speech recognition performance. However, they are computationally expensive and unnecessarily complex for keyword spotting, a task targeted to small-footprint devices. This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task. We propose a novel ResNet-based model by swapping the residual blocks by temporal Lambda layers. Furthermore, the proposed architecture is built upon uni-dimensional temporal convolutions that further reduce its complexity. The presented model does not only reach state-of-the-art accuracies on the Google Speech Commands dataset, but it is 85% and 65% lighter than its Transformer-based (KWT) and convolutional (ResNet15) counterparts while being up to 100× faster. To the best of our knowledge, this is the first attempt to explore the Lambda framework within the speech domain and therefore, we unravel further research of new interfaces based on this architecture.
Automated Software for Crewed Spacecraft—Bridging the Gap from Sci Fi to Reality

With a voice command or a few taps on the console, the spacecraft pivots on a dime at high velocity and gently docks to an orbiting space platform. This is the image most people have of the complex software computations and integrated hardware performance necessary for a spacecraft to successfully perform an automated launch, rendezvous, and docking. Today’s reality is that while computer operations are advancing rapidly, science fiction over-simplifies and over-sells current capabilities. This paper discusses the integration of spacecraft computer automation into the operation of one of the United States’ new Commercial Crew vehicles—the Boeing CST-100 Starliner. Lessons learned by the Boeing Mission Operations team, a unique private–public partnership with NASA, from conceptual design through real-time operation of the first test flight will be discussed along with evolution of the system to prepare for the second uncrewed test flight. Focus will center on how operations have learned to use the automated software to their advantage while also knowing how to adjust the automation in response to spacecraft anomalies. One goal of advanced spacecraft automation is the ability to reduce both the crew workload and the ground control footprint while at the same time increasing spacecraft and mission flexibility. Historically, crewed spacecraft required many operators on the ground to use a plethora of tools to compute nominal and contingency mission trajectories. Moving those sophisticated software tools to being onboard the vehicle can reduce the need for such complex ground support. Given that today’s spacecraft software is not yet as capable or as flexible in all circumstances as the computers depicted in movies, there is usually a trade-off between software automation cost and the flexibility of that software resulting in compromises between what is performed on the spacecraft and what is left to onboard crew or ground control. An additional challenge discussed in this paper is the added complexity when the system is still evolving in a developmental program. For missions that go beyond the Moon, software that autonomously controls nearly every aspect of a crewed mission will become a necessity, given the long-time delays between the spacecraft and Earth’s ground control teams. The lessons learned by Boeing and its Mission Operations team, through the design and implementation of Starliner’s hardware and software automation, will be able to inform future public and private spacecraft design. As the technologies and capabilities evolve, incorporating lessons learned in successful low Earth orbit commercial crew vehicle missions, spacecraft designs will continue to improve and be able to better enable safe execution of human missions to the Moon and beyond.
Signature Schemes

In the real world, when a message needs to be bound with an individual, the message is written on paper, and then the individual signs the message. Therefore, it is known that this specific message belongs to a specific person. In the digital world, things can happen similarly: a piece of data (message) is created by an individual, and then the message is signed digitally with a specific signature by that individual.
Fine-grained Emotional Analysis and Intelligent Identification of Electric Power Public Opinion Based on Attention Mechanism

In the Internet age, information explodes exponentially. As an electric power industry closely related to national life, network public opinion in the electric power field also spreads rapidly. How to fully tap the public opinion information of electric power to promote the improvement of electric power service is an important research topic. According to the research status of power public opinion, this paper defines the problem in fine-grained emotion analysis, collects the corresponding data, and defines the evaluation aspects and evaluation levels. In terms of solution strategy, according to the dimension of the problem, this paper adopts a two-way LSTM model integrating attention mechanism. The experimental results show that this method is effective in the fine-grained emotion analysis task of power public opinion samples.
Rapid Labels: Point-Feature Labeling on GPU

Labels, short textual annotations are an important component of data visualizations, illustrations, infographics, and geographical maps. In interactive applications, the labeling method responsible for positioning the labels should not take the resources from the application itself. In other words, the labeling method should provide the result as fast as possible. In this work, we propose a greedy point-feature labeling method running on GPU. In contrast to existing methods that position the labels sequentially, the proposed method positions several labels in parallel. Yet, we guarantee that the positioned labels will not overlap, nor will they overlap important visual features. When the proposed method is searching for the label position of a point-feature, the available label candidates are evaluated with respect to overlaps with important visual features, conflicts with label candidates of other point-features, and their ambiguity. The evaluation of each label candidate is done in constant time independently from the number of point-features, the number of important visual features, and the resolution of the created image. Our measurements indicate that the proposed method is able to position more labels than existing greedy methods that do not evaluate conflicts between the label candidates. At the same time, the proposed method achieves a significant increase in performance. The increase in performance is mainly due to the parallelization and the efficient evaluation of label candidates.