Reviewerly/exHarmony huggingface dataset:
Vibration Analysis for Anomaly Detection in Unmanned Aircraft

Vibration analysis is a vital measurement tool to provide detailed examination of drone health status by examining signal levels and frequencies. As drones are progressively operating in susceptible airspace where their being might cause harm, signal processing of in-flight data is becoming a necessity to reduce drone risks in sensitive conditions. On that account, this paper investigates how vibration measurements from different flights can be analysed to infer the condition of elements inside the drone. The results should assist safety operators to ascertain whether vibration anomalies can be an indicator of diagnostic and troubleshooting tools of major fault progress in drone flights. In order to track and monitorize the anomalies on the flying drone, this research proposes a vibration spectrum analysis on the inputs from on-board vibration monitoring sensors. The reason for using this analysis is that it can conduct the anomaly detection by providing critical frequency information pinpointing the faulty conditions on the drone platform. The results provides support for the proposed framework, with the ability to determine increasing defect from an unsteady flight with high payload but those results being preliminary to further research. This suggests that further drone safety research can use the same signal processing themes regarding vibration related anomalies when operating in sensitive flight zones.
Application of Fuzzy Servqual Method to Measure User Satisfaction of Mooc Service Quality

To determine the level of success and effectiveness of MOOC in the learning system, it is important to measure the quality of service. The aim of this study is to measure the perceptions and expectations of MOOC users. The method of this study is the SERVQUAL model which is combined with a fuzzy approach, so that it can be measured accurately. From the calculation, the results show that Reliability dimension has the largest gap with a gap value (-13,2325), so there are needed an improvement by the university. While Assurance dimension has the smallest gap with a value of gap (-10,0025) this is considered by the user to be a better dimension than the others. From the calculation of the overall gap value obtained, the result shows a value of -57.65, which means that the quality of service received by users is not in line with expectations.
Implementasi Algoritma Advanced Encryption Standard (AES) pada Layanan SMS Desa

Along with the development of technology, Short Message Service (SMS) has begun to be used to communicate between someone and the system in an agency. But in some cases, the security of messages sent through the SMS application has not been well protected. To improve data security and confidentiality, cryptographic algorithms with Advanced Encryption Standard (AES) can be done. The method used is the Waterfall method. AES encryption testing is done by comparing the manual calculations and the results of the encryption on the system. Blackbox test, CrackStation test, and Avalanche Effect (AE) test were also carried out. Brute force test results using CrackStation software that ciphertext cannot be solved. And in the avalanche effect (AE) test, the AE value of each 128-bit AES key is 44.53%, 192-bit is 48.44%, and 256-bit is 56.25%. Therefore, 192-bit and 256-bit AES keys are recommended for use because AE values are in the range of 45% - 60%.
Distilling Structured Knowledge for Text-Based Relational Reasoning

There is an increasing interest in developing text-based relational reasoning systems, which are capable of systematically reasoning about the relationships between entities mentioned in a text.However, there remains a substantial performance gap between NLP models for relational reasoning and models based on graph neural networks (GNNs), which have access to an underlying symbolic representation of the text.In this work, we investigate how the structured knowledge of a GNN can be distilled into various NLP models in order to improve their performance.We first pre-train a GNN on a reasoning task using structured inputs and then incorporate its knowledge into an NLP model (e.g., an LSTM) via knowledge distillation.To overcome the difficulty of crossmodal knowledge transfer, we also employ a contrastive learning based module to align the latent representations of NLP models and the GNN.We test our approach with two state-ofthe-art NLP models on 12 different inductive reasoning datasets from the CLUTRR benchmark and obtain significant improvements.
Digital Images Unveil Geometric Structures in Pairs of Relatively Prime Numbers

We present a transformation, based on the B\'ezout's identity, which maps the set of pairs of relatively prime numbers $(p,q)$ with fixed $p$ and $0<q<p$, to pairs of relatively prime numbers in the $p\times p$ square in $\mathbb R^2$, in such a way that intriguing quadratic arcs show up. We exhibit parametrizations of quadratic curves which fit such quadratic arcs and we also justify algebraically the ensuing geometry.
Comprehensive Multiparty Session Types

Multiparty session types (MST) are a well-established type theory that describes the interactive structure of a fixed number of components from a global point of view and type-checks the components through projection of the global type onto the participants of the session. They guarantee communicationsafety for a language of multiparty sessions (LMS), i.e., distributed, parallel components can exchange values without deadlocking and unexpected message types. Several variants of MST and LMS have been proposed to study key features of distributed and parallel programming. We observe that the population of the considered variants follows from only one ancestor, i.e., the original LMS/MST, and there are overlapping traits between features of the considered variants and the original. These hamper evolution of session types and languages and their adoption in practice. This paper addresses the following question: What are the essential features for MST and LMS, and how can these be modelled with simple constructs? To the best of our knowledge, this is the first time this question has been addressed. We performed a systematic analysis of the features and the constructs in MST, LMS, and the considered variants to identify the essential features. The variants are among the most influential (according to Google Scholar) and well-established systems that cover a wide set of areas in distributed, parallel programming. We used classical techniques of formal models such as BNF, structural congruence, small step operational semantics and typing judgments to build our language and type system. Lastly, the coherence of operational semantics and type system is proven by induction. This paper proposes a set of essential features, a language of structured interactions and a type theory of comprehensive multiparty session types, including global types and type system. The analysis removes overlapping features and captures the shared traits, thereby introducing the essential features. The constructs of the language are simple and fundamental, based on the $\lambda$ and $\pi$ calculi. Analogously, our global types reflect what is omitted and introduced in the language. Our system covers all the features of the original and variants, with a better ratio of the number of language and type constructs over the number of covered features. The features of the original, variants, and our system along with the number of constructs in the respective language and global types to model them are presented through a table. The syntax, operational semantics, meta-theory and type system of our system are given. We modelled all the motivating examples of the variants in our model, describing the reduction and typing steps. The work discusses how new features, in particular the non-essential ones (formerly excluded) and advanced ones can be either modelled atop the essential ones or added with minimal efforts, i.e. without modifying the existing ones. The fundamental properties of typed processes such as subject reduction, communication safety, and progress are established.
Davies theory for teleportation of quantum Fisher information under decoherence

We have investigated how memory effects on the teleportation of quantum Fisher information(QFI) for a single qubit system using a class of X-states as resources influenced by decoherence channels with memory, including amplitude damping, phase-damping and depolarizing channels. Resort to the definition of QFI, we first derive the explicit analytical results of teleportation of QFI with respect to weight parameter $\theta$ and phase parameter $\phi$ under the decoherence channels. Component percentages, the teleportation of QFI for a two-qubit entanglement system has also been addressed. The remarkable similarities and differences among these two situations are also analyzed in detail and some significant results are presented.
Multi-dimensional wavelets for scalable image decomposition: Orbital wavelets

Wavelets are closely related to the Schr\"odinger's wave functions and the interpretation of Born. Similarly to the appearance of atomic orbital, it is proposed to combine anti-symmetric wavelets into orbital wavelets. The proposed approach allows the increase of the dimension of wavelets through this process. New orbital 2D-wavelets are introduced for the decomposition of still images, showing that it is possible to perform an analysis simultaneous in two distinct scales. An example of such an image analysis is shown.
Copy Mechanism and Tailored Training for Character-Based Data-to-Text Generation

In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation. The most widely used sequence-to-sequence neural methods are word-based: as such, they need a pre-processing step called delexicalization (conversely, relexicalization) to deal with uncommon or unknown words. These forms of processing, however, give rise to models that depend on the vocabulary used and are not completely neural. In this work, we present an end-to-end sequence-to-sequence model with attention mechanism which reads and generates at a character level, no longer requiring delexicalization, tokenization, nor even lowercasing. Moreover, since characters constitute the common “building blocks” of every text, it also allows a more general approach to text generation, enabling the possibility to exploit transfer learning for training. These skills are obtained thanks to two major features: (i) the possibility to alternate between the standard generation mechanism and a copy one, which allows to directly copy input facts to produce outputs, and (ii) the use of an original training pipeline that further improves the quality of the generated texts. We also introduce a new dataset called E2E+, designed to highlight the copying capabilities of character-based models, that is a modified version of the well-known E2E dataset used in the E2E Challenge. We tested our model according to five broadly accepted metrics (including the widely used bleu), showing that it yields competitive performance with respect to both character-based and word-based approaches.
Comparative Analysis of Bilateral Filter and its Variants for Magnetic Resonance Imaging

Background: With the increase in research in the direction of making images noise free a number of algorithms have been designed. Methods: The choice of Denoising method will be made in such a way that it reduces or removes noise content on one hand and on the other hand it preserves the information content of the image. Our article focuses on analyzing the performance of bilateral filters and its derivatives for Denoising of MRI images. Results: The bilateral filter is a hybridized version of basic range filtering and domain filtering techniques. Conclusion: A comparative review of these filters is presented for MRI images taking different altitudes of Gaussian noise added to the image.
Truecasing German user-generated conversational text

True-casing, the task of restoring proper case to (generally) lower case input, is important in downstream tasks and for screen display. In this paper, we investigate truecasing as an in- trinsic task and present several experiments on noisy user queries to a voice-controlled dia- log system. In particular, we compare a rule- based, an n-gram language model (LM) and a recurrent neural network (RNN) approaches, evaluating the results on a German Q&A cor- pus and reporting accuracy for different case categories. We show that while RNNs reach higher accuracy especially on large datasets, character n-gram models with interpolation are still competitive, in particular on mixed- case words where their fall-back mechanisms come into play.
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding

We look into the task of generalizing word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, without extra contextual information.We rely solely on the spellings of words and propose a model, along with an efficient algorithm, that simultaneously models subword segmentation and computes subword-based compositional word embedding.We call the model probabilistic bag-of-subwords (PBoS), as it applies bag-of-subwords for all possible segmentations based on their likelihood.Inspections and affix prediction experiment show that PBoS is able to produce meaningful subword segmentations and subword rankings without any source of explicit morphological knowledge.Word similarity and POS tagging experiments show clear advantages of PBoS over previous subword-level models in the quality of generated word embeddings across languages.
Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction

Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot.These noisy, dense, and domain-specific lab protocols processing draws more and more interests with the development of deep learning.This paper presents our teamwork on WNUT 2020 shared task-1: wet lab entity extract, that we conducted studies in several models, including a BiLSTM CRF model and a Bert case model which can be used to complete wet lab entity extraction.And we mainly discussed the performance differences of Bert case under different situations such as transformers versions, case sensitivity that may don't get enough attention before.
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads

Deep pre-trained Transformer models have achieved state-of-the-art results over a variety of natural language processing (NLP) tasks. By learning rich language knowledge with millions of parameters, these models are usually overparameterized and significantly increase the computational overhead in applications. It is intuitive to address this issue by model compression. In this work, we propose a method, called Single-Shot Meta-Pruning, to compress deep pre-trained Transformers before fine-tuning. Specifically, we focus on pruning unnecessary attention heads adaptively for different downstream tasks. To measure the informativeness of attention heads, we train our Single-Shot Meta-Pruner (SMP) with a meta-learning paradigm aiming to maintain the distribution of text representations after pruning. Compared with existing compression methods for pre-trained models, our method can reduce the overhead of both fine-tuning and inference. Experimental results show that our pruner can selectively prune 50% of attention heads with little impact on the performance on downstream tasks and even provide better text representations. The source code will be released in the future.
PDS: Deduce elder privacy from smart homes

With the development of IoT technologies in the past few years, a wide range of smart devices are deployed in a variety of environments aiming to improve the quality of human life in a cost efficient way. Due to the increasingly serious aging problem around the world, smart homes for elder healthcare have become an important IoT-based application, which not only enables elders’ health to be properly monitored and taken care of, but also allows them to live more comfortably and independently in their houses. However, elders’ privacy might be disclosed from smart homes due to non-fully protected network communication. To show that elders’ privacy could be substantially exposed, in this paper we develop a Privacy Deduction Scheme (PDS for short) by eavesdropping sensor traffic from a smart home to identify elders’ movement activities and speculating sensor locations in the smart home based on a series of deductions from the viewpoint of an attacker. The experimental results based on sensor datasets from real smart homes demonstrate the effectiveness of PDS in deducing and disclosing elders’ privacy, which might be maliciously exploited by attackers to endanger elders and their properties.
Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics

The effective utilization at scale of complex machine learning (ML) techniques for HEP use cases poses several technological challenges, most importantly on the actual implementation of dedicated end-to-end data pipelines. A solution to these challenges is presented, which allows training neural network classifiers using solutions from the Big Data and data science ecosystems, integrated with tools, software, and platforms common in the HEP environment. In particular, Apache Spark is exploited for data preparation and feature engineering, running the corresponding (Python) code interactively on Jupyter notebooks. Key integrations and libraries that make Spark capable of ingesting data stored using ROOT format and accessed via the XRootD protocol, are described and discussed. Training of the neural network models, defined using the Keras API, is performed in a distributed fashion on Spark clusters by using BigDL with Analytics Zoo and also by using TensorFlow, notably for distributed training on CPU and GPU resources. The implementation and the results of the distributed training are described in detail in this work.
Several trade off features of quantum steering in distributed scenario

In the present work we address the question of how bipartite steering violation takes place among multi-partite systems (where each sub-system have Hilbert space dimension restricted to two) based on the maximal violations of the bipartite steering inequality of the reduced pairwise qubit systems. We have derived a trade-off relation which is satisfied by those pair wise bipartite maximal steering violations, which physically can be understood as providing restrictions on the distribution of steering among sub systems. For a three-qubit system, it is impossible that all pairs of qubits violate the steering inequality, and once a pair of qubits violates the steering inequality maximally, the other two pairs of qubits must both obey the steering inequality. We also present a complementarity relation between genuine entanglement present in a tripartite state and maximum bipartite steering violation by its reduced states.
Distribution of spin correlation strengths in multipartite systems

For a two-qubit state the isotropic strength measures the degree of isotropic spin correlation. The concept of isotropic strength is generalized to multipartite qudit systems, and the strength distributions for tripartite and quadripartite qudit systems are thoroughly investigated. We show that the sum of relative isotropic strengths of any three qudit state over $d$-dimensional Hilbert space cannot exceed $d-1$, which generalizes of the case $d=2$. The trade-off relations and monogamy-like relations of the sum of spin correlation strengths for pure three- and four-partite systems are derived. Moreover, the bounds of spin correlation strengths among different subsystems of a quadripartite state are used to analyze quantum entanglement.
Umbra: A Visual Analysis Approach for Defense Construction Against Inference Attacks on Sensitive Information

Collecting and analyzing anonymous personal information is required as a part of data analysis processes, such as medical diagnosis and restaurant recommendation. Such data should ostensibly be stored so that specific individual information cannot be disclosed. Unfortunately, inference attacks-integrating background knowledge and intelligent models-hinder classic sanitization techniques like syntactic anonymity and differential privacy from exhaustively protecting sensitive information. As a solution, we introduce a three-stage approach empowered within a visual interface, which depicts underlying inference behaviors via a Bayesian Network and supports a customized defense against inference attacks from unknown adversaries. In particular, our approach visually explains the process details of the underlying privacy preserving models, allowing users to verify if the results sufficiently satisfy the requirements of privacy preservation. We demonstrate the effectiveness of our approach through two case studies and expert reviews.
Improved point center algorithm for K-Means clustering to increase software defect prediction

The k-means is a clustering algorithm that is often and easy to use. This algorithm is susceptible to randomly chosen centroid points so that it cannot produce optimal results. This research aimed to improve the k-means algorithm’s performance by applying a proposed algorithm called point center. The proposed algorithm overcame the random centroid value in k-means and then applied it to predict software defects modules’ errors. The point center algorithm was proposed to determine the initial centroid value for the k-means algorithm optimization. Then, the selection of X and Y variables determined the cluster center members. The ten datasets were used to perform the testing, of which nine datasets were used for predicting software defects. The proposed center point algorithm showed the lowest errors. It also improved the k-means algorithm’s performance by an average of 12.82% cluster errors in the software compared to the centroid value obtained randomly on the simple k-means algorithm. The findings are beneficial and contribute to developing a clustering model to handle data, such as to predict software defect modules more accurately.
On the Usage of the Trifocal Tensor in Motion Segmentation

Motion segmentation, i.e., the problem of clustering data in multiple images based on different 3D motions, is an important task for reconstructing and understanding dynamic scenes. In this paper we address motion segmentation in multiple images by combining partial results coming from triplets of images, which are obtained by fitting a number of trifocal tensors to correspondences. We exploit the fact that the trifocal tensor is a stronger model than the fundamental matrix, as it provides fewer but more reliable matches over three images than fundamental matrices provide over the two. We also consider an alternative solution which merges partial results coming from both triplets and pairs of images, showing the strength of three-frame segmentation in a combination with two-frame segmentation. Our real experiments on standard as well as new datasets demonstrate the superior accuracy of the proposed approaches when compared to previous techniques .