Reviewerly/exHarmony huggingface dataset:
Which priors matter? Benchmarking models for learning latent dynamics

Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving. In these settings, ML algorithms typically need to reason about a physical system using high dimensional observations, such as images, without access to the underlying state. Recently, several methods have proposed to integrate priors from classical mechanics into ML models to address the challenge of physical reasoning from images. In this work, we take a sober look at the current capabilities of these models. To this end, we introduce a suite consisting of 17 datasets with visual observations based on physical systems exhibiting a wide range of dynamics. We conduct a thorough and detailed comparison of the major classes of physically inspired methods alongside several strong baselines. While models that incorporate physical priors can often learn latent spaces with desirable properties, our results demonstrate that these methods fail to significantly improve upon standard techniques. Nonetheless, we find that the use of continuous and time-reversible dynamics benefits models of all classes.
Pattern Recognition and Stylometry Analysis of Pathittrupathu in Tamil Literature

This research paper attempts to identify the structure and clustering of Sangam Literature Pathittrpathu in Tamil using Data Mining Techniques. Pathittrupathu means, it is split into ten tens poems, which is a poetic work and one of the eight anthologies available in ancientTamil literature. This work originally contains ten divisions of ten poems each dedicated tovarious Chera kings rule of Tamil, except the poetic works of first and the last ten poems have been lost in before sangam period of Tamil. The remain eight poetic works is considered as a database in this research paper. Twenty parameters were used to identify the structure and pattern of Sangam literature of Tamil. Initially, usage of words is identifiedusing descriptive statistics and pattern of poetic structure is traced by using hierarchical data mining techniques. The result shows that all the eight Pathittrupathu split into two cluster and their writing styles were unique. The clustering methods achieved same clusters and named as natural clusters.
A Self-Adaptive Hybrid Bat Algorithm for Training Feedforward Neural Networks

Training feedforward neural network (FFNN) is a complex task in the supervised learning field. An FFNN trainer aims to find the best set of weights that minimizes classification error. This paper presents a new training method based on hybrid bat optimization with self-adaptive differential evolution to train the feedforward neural networks. The hybrid training algorithm combines bat and the self-adaptive differential evolution algorithm called BAT-SDE. BAT-SDE is used to better search in the solution space, which proves its effectiveness in large space solutions. The performance of the proposed approach was compared with eight evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using seven bio-medical datasets and one large credit card fraud detection dataset. The results of the comparative study show that BAT-SDE outperformed other training methods in most datasets and can be an alternative to other training methods.
Graph Attention Mechanism with Cardinality Preservation for Knowledge Graph Completion

Embedding knowledge graph with graph attention network has become a novel research topic in the field of knowledge graph completion. However, the current graph attention network generates the same embeddings for different structures and different entities when generating entity embeddings for knowledge graph. The quality of embedding directly contributes the effective of completion. We analyze the reason why graph attention network cannot distinguish structure, because the aggregation based on attention GNN ignores the cardinality information, which is the mapping of diverse features, and helps to distinguish the contributions of different nodes in the domain. Therefore, we propose the graph attention preserving (KBCPA) model. Cardinality information is added into the attentional mechanism based aggregation to generate different representations for different entities, thus improving the discrimination ability of the model. Our experiments present that our model is effective and competitive, and it obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237.
Taming Tail Latency in Key-Value Stores: A Scheduling Perspective

Distributed key-value stores employ replication for high availability. Yet, they do not always efficiently take advantage of the availability of multiple replicas for each value, and read operations often exhibit high tail latencies. Various replica selection strategies have been proposed to address this problem, together with local request scheduling policies. It is difficult, however, to determine what is the absolute performance gain each of these strategies can achieve. We present a formal framework allowing the systematic study of request scheduling strategies in key-value stores. We contribute a definition of the optimization problem related to reducing tail latency in a replicated key-value store as a minimization problem with respect to the maximum weighted flow criterion. By using scheduling theory, we show the difficulty of this problem, and therefore the need to develop performance guarantees. We also study the behavior of heuristic methods using simulations, which highlight which properties are useful for limiting tail latency: for instance, the EFT strategy—which uses the earliest available time of servers—exhibits a tail latency that is less than half that of state-of-the-art strategies, often matching the lower bound. Our study also emphasizes the importance of metrics such as the stretch to properly evaluate replica selection and local execution policies.
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance

The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system.Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking.However, such systems consider only confidence estimates and have difficulty scaling to more complex settings.Neural dialogue systems, on the other hand, rarely take uncertainties into account.They are therefore overconfident in their decisions and less robust.Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation.We propose the use of different uncertainty measures in neural belief tracking.The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator.Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy.This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.
A Connected Component-Based Deep Learning Model for Multi-type Struck-Out Component Classification

Due to the presence of struck-out handwritten words in document images, the performance of different methods degrades for several important applications, such as handwriting recognition, writer, gender, fraudulent document identification, document age estimation, writer age estimation, normal/abnormal behavior of person analysis, and descriptive answer evaluation. This work proposes a new method which combines connected component analysis for text component detection and deep learning for classification of struck-out and non-struck-out words. For text component detection, the proposed method finds the stroke width to detect edges of texts in images, and then performs smoothing operations to remove noise. Furthermore, morphological operations are performed on smoothed images to label connected components as text by fixing bounding boxes. Inspired by the great success of deep learning models, we explore DenseNet for classifying struck-out and non-struck-out handwritten components by considering text components as input. Experimental results on our dataset demonstrate the proposed method outperforms the existing methods in terms of classification rate.
Syntactic Analysis of Kimbeere Relative Clauses

This paper looks at the syntax of relative clauses in Kimbere, an SVO Bantu Language spoken in Kenya.From the evidence of interpretation of bound pronouns, raising with idiomatic expressions, and sectional restrictions between demonstratives and relatives clauses, it is concluded that Kimbeere relative clauses can be easily analyzed within Kayne's Head raising analysis of Relative Clauses.
Talk, Don&amp;#8217;t Write: A Study of Direct Speech-Based Image Retrieval

Speech-based image retrieval has been studied as a proxy for joint representation learning, usually without emphasis on retrieval itself. As such, it is unclear how well speech-based retrieval can work in practice -- both in an absolute sense and versus alternative strategies that combine automatic speech recognition (ASR) with strong text encoders. In this work, we extensively study and expand choices of encoder architectures, training methodology (including unimodal and multimodal pretraining), and other factors. Our experiments cover different types of speech in three datasets: Flickr Audio, Places Audio, and Localized Narratives. Our best model configuration achieves large gains over state of the art, e.g., pushing recall-at-one from 21.8% to 33.2% for Flickr Audio and 27.6% to 53.4% for Places Audio. We also show our best speech-based models can match or exceed cascaded ASR-to-text encoding when speech is spontaneous, accented, or otherwise hard to automatically transcribe.
Near-Perfect Relation Extraction from Family Books

Precise sequence constraints are proposed to accelerate information extraction from a class of “semi-structured” documents that includes hundreds of thousands of digitized genealogical records. While Named Entity Recognition (NER) and Named Relation Recognition (NRR) on free-running text lack universally applicable solutions, under these constraints generalized template-matching can accomplish both. Interactive information extraction is demonstrated on three digitized books. The book-text tokens are first labeled according to their role (e.g. Head, Spouse, or Birthdate), then pairs of labeled entities are combined into labeled relations (e.g. <Head [Spouse]>, or <Spouse [Birthdate]>). Accurate NRR is ensured by high-precision (>99%) NER. On semi-structured text the proposed NRR algorithm produces only valid relations from correctly labeled entities. About three hours of user interaction and a few minutes of laptop run time suffice to produce database- or ontology-ready input from a new book.
Using machine learning to model the training scalability of convolutional neural networks on clusters of GPUs

Abstract In this work, we build a general piece-wise model to analyze data-parallel (DP) training costs of convolutional neural networks (CNNs) on clusters of GPUs. This general model is based on i) multi-layer perceptrons (MLPs) in charge of modeling the NVIDIA cuDNN/cuBLAS library kernels involved in the training of some of the state-of-the-art CNNs; and ii) an analytical model in charge of modeling the NVIDIA NCCL Allreduce collective primitive using the Ring algorithm. The CNN training scalability study performed using this model in combination with the Roofline technique on varying batch sizes, node (floating-point) arithmetic performance, node memory bandwidth, network link bandwidth, and cluster dimension unveil some crucial bottlenecks at both GPU and cluster level. To provide evidence of this analysis, we validate the accuracy of the proposed model against a Python library for distributed deep learning training.
Optimal Rates of Teaching and Learning Under Binary Symmetric Noise

In this paper, we consider a recently-proposed model of teaching and learning under uncertainty, in which a teacher receives independent observations of a single bit corrupted by binary symmetric noise, and sequentially transmits to a student through another binary symmetric channel based on the bits observed so far. After a given number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> of transmissions, the student outputs an estimate of the unknown bit, and we are interested in the exponential decay rate of the error probability as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> increases. We propose a novel block-structured teaching strategy in which the teacher encodes the number of 1s received in each block, and show that the resulting error exponent is the binary relative entropy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D(\frac{1}{2}\Vert\max(p,\ q))$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> are the noise parameters. This matches a trivial converse result based on the data processing inequality, and settles two conjectures of [Jog and Loh, 2021] and [Huleihel et al., 2019]. In addition, we show that the computation time required by the teacher and student is linear in n.
Sniffers and Social Engineering

In this chapter, you will learn about sniffing and how this technique is used. You will gain an understanding of protocols that may be vulnerable to sniffing and how to detect types of sniffing attacks. In this chapter, you will also be introduced to countermeasures for sniffing. You will also learn about different types of social engineering plus countermeasures to protect individuals from attack.
BESOCIAL: A Sustainable Knowledge Graph-Based Workflow for Social Media Archiving

Social media as infrastructure for public discourse provide valuable information that needs to be preserved. Several tools for social media harvesting exist, but still only fragmented workflows may be formed with different combinations of such tools. On top of that, social media data but also preservation-related metadata standards are heterogeneous, resulting in a costly manual process. In the framework of BESOCIAL at the Royal Library of Belgium (KBR), we develop a sustainable social media archiving workflow that integrates heterogeneous data sources in a Europeana and PREMIS-based data model to describe data preserved by open source tools. This allows data stewardship on a uniform representation and we generate metadata records automatically via queries. In this paper, we present a comparison of social media harvesting tools and our Knowledge Graph-based solution which reuses off-the-shelf open source tools to harvest social media and automatically generate preservation-related metadata records. We validate our solution by generating Encoded Archival Description (EAD) and bibliographic MARC records for preservation of harvested social media collections from Twitter collected at KBR. Other archiving institutions can build upon our solution and customize it to their own social media archiving policies.
Recoverable Private Key Scheme for Consortium Blockchain Based on Verifiable Secret Sharing

As a current popular technology, the blockchain has a serious issue: the private key cannot be retrieved due to force majeure.Since the outcome of the blockchain-based Bitcoin, there have been many occurrences of the users who lost or forgot their private keys and could not retrieve their token wallets, and it may cause the permanent loss of their corresponding blockchain accounts, resulting in irreparable losses for the users.We propose a recoverable private key scheme for consortium blockchain based on the verifiable secret sharing which can enable the user's private key in the consortium blockchain to be securely recovered through a verifiable secret sharing method.In our secret sharing scheme, users use the biometric keys to encrypt shares, and the preset committer peers in the consortium blockchain act as the participants to store the users' private key shares.Due to the particularity of the biometric key, only the user can complete the correct secret recovery.Our comparisons with the existing mnemonic systems or the multi-signature schemes have shown that our scheme can allow users to recover their private keys without storing the passwords accurately.Hence, our scheme can improve the account security and recoverability of the data-sharing systems across physical and virtual platforms that use blockchain technology.
Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards. One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight. It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods. Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.
Amazon Pharmacy: distraction or disruption?

After years of anticipation about Amazon's rumored entry into pharmacy, Amazon Pharmacy launched in November 2020. What is yet to be understood is whether this new Amazon offering is a true market disruption capable of upending the pharmacy industry. This commentary describes the epic rise of Amazon from bookseller to retail giant, leading to its entry into the retail pharmacy space. Amazon Pharmacy's business model is described and its potential for industry disruption discussed.
Vietnamese Sentence Paraphrase Identification using Pre-trained Model and Linguistic Knowledge

The paraphrase identification task identifies whether two text segments share the same meaning, thereby playing a crucial role in various applications, such as computerassisted translation, question answering, machine translation, etc.Although the literature on paraphrase identification in English and other popular languages is vast and growing, the research on this topic in Vietnamese remains relatively untapped.In this paper, we propose a novel method to classify Vietnamese sentence paraphrases, which deploys both the pre-trained model to exploit the semantic context and linguistic knowledge to provide further information in the identification process.Two branches of neural networks built in the Siamese architecture are also responsible for learning the differences among the sentence representations.To evaluate the proposed method, we present experiments on two existing Vietnamese sentence paraphrase corpora.The results show that for the same corpora, our method using the PhoBERT as a feature vector yields 94.97% F1-score on the VnPara corpus and 93.49% F1-score on the VNPC corpus.They are better than the results of the Siamese LSTM method and the pre-trained models.
Detecting English Speech in the Air Traffic Control Voice Communication

We launched a community platform for collecting the ATC speech world-wide in the ATCO2 project. Filtering out unseen non-English speech is one of the main components in the data processing pipeline. The proposed English Language Detection (ELD) system is based on the embeddings from Bayesian subspace multinomial model. It is trained on the word confusion network from an ASR system. It is robust, easy to train, and light weighted. We achieved 0.0439 equal-error-rate (EER), a 50% relative reduction as compared to the state-of-the-art acoustic ELD system based on x-vectors, in the in-domain scenario. Further, we achieved an EER of 0.1352, a 33% relative reduction as compared to the acoustic ELD, in the unseen language (out-of-domain) condition. We plan to publish the evaluation dataset from the ATCO2 project.
Clean-AR: Using Augmented Reality for Reducing the Risk of Contamination from Airborne Disease Agents on Surfaces

Abstract A core principle of modern health care is the compliance of hygienic and aseptic techniques in areas that are sensitive to contamination through bacteria, dust, aerosols, and fallout, primarily in operating theatres or around patients with contagious diseases. Keeping track of potentially contaminated surfaces in an environment is a major concern, especially when protecting from COVID-19. This work proposes a novel concept in using 3D sensing technology to track human movement within an indoor area and identifying high-risk contaminated surfaces in real-time. It combines recent Augmented Reality display technology, which allows keeping track of decontaminated surfaces during the cleaning process using an interactive visualization method. The proposed concept of Clean- AR is implemented in a clinical environment used for observation in COVID-19 scenarios. We discuss key challenges and outline further research direction in effectively reducing the risk of contamination using the proposed concept.
Vision Based Self-Guided Quadcopter Landing on Moving Platform During Fault Detection

Fault occurrence in the quadcopter is very common during operation in the air. This paper presents a real-time implementation to detect the fault and then the system is guaranteeing to safely land on the surface, even the moving landing platform. Primarily, PixHawk auto-pilot was used to verify in real-time, with platform detection and various environmental conditions. The method is ensuring the quadcopter operates in the landing area zone with the help of a GPS feature. Then the precise landing on the astable-landing platform is calibrated automatically using the vision-based learning feedback technique. The proposed objective is developed using reconfigurable Raspberry Pi-3 with a Pi camera. The full decision on an efficient landing algorithm is deployed into the quadcopter. The system is self-guided and automatically returns to home-based whenever the fault detects. The study is conducted with the situation of low battery operation and the trigger of auto-pilot helps to land the device safely before any mal-function. The system is featured with predetermined speed and altitude while navigating the home base, thus improves the detection process. Finally, the experiment study provided successful trials to track usable platform, landing on a restricted area, and disarm the motors autonomously.