Reviewerly/exHarmony huggingface dataset:
On the Same Page? Comparing Inter-Annotator Agreement in Sentence and Document Level Human Machine Translation Evaluation.

Document-level evaluation of machine translation has raised interest in the community especially since responses to the claims of “human parity” (Toral et al., 2018; Laubli et al., 2018) with document-level human evaluations have been published. Yet, little is known about best practices regarding human evaluation of machine translation at the document-level. This paper presents a comparison of the differences in inter-annotator agreement between quality assessments using sentence and document-level set-ups. We report results of the agreement between professional translators for fluency and adequacy scales, error annotation, and pair-wise ranking, along with the effort needed to perform the different tasks. To best of our knowledge, this is the first study of its kind.
Design, analysis and implementation of a smart next generation secure shipping infrastructure using autonomous robot

In general, price is the key element in shipping, and half of the costs are tied up in last-mile deliveries. The biggest expense here is the human element, so companies, which can cut down on staff costs, will be able to out-price their competitors. Therefore, we expect sooner, or later robotic delivery systems will become the norm. However, ensuring security in such a system will be a challenge. In this article, we propose a secure shipping infrastructure using robot. In this regard, we first design a cooperative user authentication system for delivering parcel using crypto primitives such as one-way hash function, asymmetric encryption and QR code. Next, we design a non-cooperative user identification scheme using Siamese Network for Person Reidentification, where the client is not ready to cooperate during the authentication process. Therefore, the robot carrier needs to automatically recognize and go towards the client to deliver the parcel in a secure way. Finally, we implement the proof-of-concept system through a cooperative user authentication process using TurtleBot3 robot platform and analyse the security of the proposed scheme using ProVerif. Analysis and experiment results show that our proposed system can complete the delivery mission and resist a variety of security attacks.
DisepNet for breast abnormality recognition

The recognition of breast abnormality, which mainly consists of mass and micro-calcification, plays a critical role in the detection of breast cancer. To facilitate the procedure of making decisions on suspicious regions in mammograms, we proposed a light-weighted deep convolutional neural network (CNN) recognition system termed DisepNet, a light-weighted fully convolutional network that shows promising performance on the detection task of breast abnormality. In the proposed DisepNet, novel blocks feature extraction are designed, which are termed as Disep block and Incep-L block respectively. We evaluated the proposed model by 5-fold cross-validation on a combined dataset, which comes from two public databases MINI-MIAS and INbreast. The final accuracy of our proposed model achieved a mean accuracy at 95.60% while the sensitivity and specificity reached 93.71% and 97.44% respectively. A comparison between our model and existing methods shows that our model provides the best performance.
Extended Study on Using Pretrained Language Models and YiSi-1 for Machine Translation Evaluation.

We present an extended study on using pretrained language models and YiSi-1 for machine translation evaluation. Although the recently proposed contextual embedding based metrics, YiSi-1, significantly outperform BLEU and other metrics in correlating with human judgment on translation quality, we have yet to understand the full strength of using pretrained language models for machine translation evaluation. In this paper, we study YiSi-1’s correlation with human translation quality judgment by varying three major attributes (which architecture; which inter- mediate layer; whether it is monolingual or multilingual) of the pretrained language mod- els. Results of the study show further improvements over YiSi-1 on the WMT 2019 Metrics shared task. We also describe the pretrained language model we trained for evaluating Inuktitut machine translation output.
Learning from distribution-changing data streams via decision tree model reuse

In many real-world applications, data are collected in the form of streams. As a result of the evolving nature of dynamic environments, the distribution of data streams generally changes over time. Such distribution changes hinder the application of conventional machine learning approaches because the fundamental assumption of independent and identical distribution does not hold in these scenarios. This paper proposes an algorithm based on the decision tree model reuse mechanism for learning from distribution-changing data streams. The proposed algorithm is essentially an online ensemble method that maintains a model pool and updates it by performing decision tree model reuse. The main idea is to exploit the useful knowledge in historical data to help resist the negative effects of distribution changes. We validate the effectiveness of the proposed approach through experiments on synthetic and real-world datasets.
Deep Software Variability: Towards Handling Cross-Layer Configuration

Configuring software is a powerful means to reach functional and performance goals of a system. However, many layers (hardware, operating system, input data, etc.), themselves subject to variability, can alter performances of software configurations. For instance, configurations’ options of the x264 video encoder may have very different effects on x264’s encoding time when used with different input videos, depending on the hardware on which it is executed. In this vision paper, we coin the term deep software variability to refer to the interaction of all external layers modifying the behavior or non-functional properties of a software. Deep software variability challenges practitioners and researchers: the combinatorial explosion of possible executing environments complicates the understanding, the configuration, the maintenance, the debug, and the test of configurable systems. There are also opportunities: harnessing all variability layers (and not only the software layer) can lead to more efficient systems and configuration knowledge that truly generalizes to any usage and context.
Identification of significant bio-markers from high-dimensional cancerous data employing a modified multi-objective meta-heuristic algorithm

Identification of the most prominent bio-markers or genes with high classification accuracy in the high-dimensional cancerous data, is still remained as an emerging challenge for the machine learning researchers. As this challenge has two objectives, i.e. minimizing the number of genes (NoG) and maximizing the classification accuracy percentage (CAP), this problem can be modeled as binary multi-objective approach. In this work, a modified version of multi-objective Jaya algorithm, multi-objective chaotic Jaya (MOCJaya), is suggested to select the minimum NoG with high CAP. Initially, a filter approach, namely Fisher score is applied to pre-select the informative genes. Then, MOCJaya algorithm is employed for both selecting key genes and classifying the cancer data. To assess the efficacy of the designed algorithm, ten binary and multi-class cancerous datasets are considered. Here, the suggested algorithm has been compared with multi-objective chaotic Genetic Algorithm (MOCGA), multi-objective chaotic particle swarm optimization (MOCPSO), multi-objective Jaya (MOJaya), multi-objective PSO (MOPSO), and non-dominated sorting GA (NSGA-II) models. Moreover, a comparison of MOCJaya algorithm with other seventeen existing models, is also performed here. The experimental results and comparison analysis reveal that MOCJaya classifies both the positive and negative samples of the cancer datasets in high CAP with a smaller NoG.
Findings of the WMT 2023 Shared Task on Automatic Post-Editing

We present the results from the 9th round of the WMT shared task on MT Automatic Post-Editing, which consists of automatically correcting the output of a “black-box” machine translation system by learning from human corrections. Like last year, the task focused on English→Marathi, with data coming from multiple domains (healthcare, tourism, and general/news). Despite the consistent task framework, this year’s data proved to be extremely challenging. As a matter of fact, none of the official submissions from the participating teams succeeded in improving the quality of the already high-level initial translations (with baseline TER and BLEU scores of 26.6 and 70.66, respectively). Only one run, accepted as a “late” submission, achieved automatic evaluation scores that exceeded the baseline.
Twitter Sentiment Analysis Using Deep Learning

Abstract It is well established that the tweet sentiment analysis is still focused on conventional messages, such as film reviews and product reviews, while significant improvement has been made as deep learning becomes widespread, and comprehensive data sets are accessible for training (far from just emoticons and hashtags). Nevertheless, prior opinion analysis experiments typically performed on tweets, i.e. only two forms of global polarities (i.e. optimistic and negative) occur with their work/validation/test data sets. What is more, systems’ judgments are not actively aligned with the specified appraisal objects. In this paper, we have discussed some deep learning approaches for twitter sentiment analysis. We also trained our model using CNN and RNN to get some good accuracy results.
Stochastic Optimization Algorithms for Data Processing in Experimental Self-heating Process

The search for parameters is carried out using stochastic optimization algorithms, namely, by the double annealing method. Self-heating simulation is based on finite element model of direct simulation of stationary heating of the specimen. The criterion for convergence of the optimization process is the data on the heating temperature obtained from field experiments. The verification of the reliability of the results is carried out by comparing the data on the area of the hysteresis loop obtained by numerical calculation and from a full-scale experiment. The paper presents the results of identification of the parameters of calculations of the thermal state, as well as the process of convergence of the search algorithm.
Probing BERT in Hyperbolic Spaces

Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embeddings lay in certain metric spaces, typically the Euclidean space. This work posits a family of geometrically special spaces, the hyperbolic spaces, that exhibit better inductive biases for hierarchical structures and may better reveal linguistic hierarchies encoded in contextualized representations. We introduce a ePoincare probe, a structural probe projecting these embeddings into a Poincare subspace with explicitly defined hierarchies. We focus on two probing objectives: (a) dependency trees where the hierarchy is defined as head-dependent structures; (b) lexical sentiments where the hierarchy is defined as the polarity of words (positivity and negativity). We apply our probes on BERT, a typical contextualized embedding model. In the syntactic subspace, our probe better recovers tree structures than Euclidean probes, revealing the possibility that the geometry of BERT syntax may not necessarily be Euclidean. In the sentiment subspace, we reveal two possible meta-embeddings for positive and negative sentiments and show how lexically-controlled contextualization would change the geometric localization of embeddings. We demonstrate the findings with our Poincare probe via extensive experiments and visualization.
Renamings and a Condition-free Formalization of Kronecker’s Construction

Summary In [7], [9], [10] we presented a formalization of Kronecker’s construction of a field extension E for a field F in which a given polynomial p ∈ F [ X ] \F has a root [5], [6], [3]. A drawback of our formalization was that it works only for polynomial-disjoint fields, that is for fields F with F ∩ F [ X ] = ∅. The main purpose of Kronecker’s construction is that by induction one gets a field extension of F in which p splits into linear factors. For our formalization this means that the constructed field extension E again has to be polynomial-disjoint. In this article, by means of Mizar system [2], [1], we first analyze whether our formalization can be extended that way. Using the field of polynomials over F with degree smaller than the degree of p to construct the field extension E does not work: In this case E is polynomial-disjoint if and only if p is linear. Using F [ X ] /&lt;p&gt; one can show that for F = ℚ and F = ℤ n the constructed field extension E is again polynomial-disjoint, so that in particular algebraic number fields can be handled. For the general case we then introduce renamings of sets X as injective functions f with dom( f ) = X and rng( f ) ∩ ( X ∪ Z ) = ∅ for an arbitrary set Z . This, finally, allows to construct a field extension E of an arbitrary field F in which a given polynomial p ∈ F [ X ] \F splits into linear factors. Note, however, that to prove the existence of renamings we had to rely on the axiom of choice.
Boundary precedence image inpainting method based on Self-organizing Maps

In addition to text data analysis, image analysis is an area that has increasingly gained importance in recent years because more and more image data have spread throughout the internet and real life. As an important segment of image analysis techniques, image restoration has been attracting a lot of researchers’ attention. As one of AI methodologies, Self-organizing Maps (SOMs) have been applied to a great number of useful applications. However, it has rarely been applied to the domain of image restoration. In this paper, we propose a novel image restoration method by leveraging the capability of SOMs, and we name it “boundary precedence image inpainting method based on SOMs”. In the proposed method, SOMs are used to separate a damaged image into different layers according to the pixel information of the image. Each pixel in the damaged area is considered to be a center of a square area, which is called a waiting-for-inpainting patch. The waiting-for-inpainting patch filling order is calculated by the boundary precedence method in which the information of the separated image layers obtained by SOMs is analyzed and used to calculate the filling order. According to the proposed method, the waiting-for-inpainting patches on the boundaries of the damaged region are restored first and the filling order of this proposed method depends on the precedence values of each waiting-for-inpainting patch. Case studies demonstrate the effectiveness of this proposed method. Both textural and structural information can be nicely repaired by the proposed method.
Motion Planning of Mobile Robots in Indoor Topological Environments using Partially Observable Markov Decision Process

Deterministic motion planners perform well in simulated environments, where sensors and actuators are perfect. However, these assumptions are restrictive and consequently motion planning will have poor performance if applied to real robotic systems (or a more realistic simulator), as they are inherently fraught with uncertainty. In most real robotic systems, states cannot be directly observed, and the results of the actions performed by the robots are uncertain. Thus, the robot must make use of a new class of planners that take into account system uncertainties when making a decision. In the present work, the Partially Observable Markov Decision Process is presented as an alternative to solve problems immersed in uncertainties, selecting optimal actions aiming to perform a given task. The contribution of this article is to implement the Partially Observable Markov Decision Process using greedy optimization, which has considerably simplified the decision-making problem for uncertain environments. This article also presents new ways to determine the parameters of the Partially Observable Markov Decision Process. The aforementioned tooling was applied in a system to control the actions of a real robot that navigates in a indoor topological living space with ambiguity of informations.
Graph Regularized Hierarchical Diffusion Process With Relevance Feedback for Medical Image Retrieval

Befitting from the interpretability and the capacity in capturing the underlying manifold structure, diffusion process (DP) has attracted increasing attention in the field of image retrieval.Within it, hierarchical diffusion process (HDP) has achieved satisfactory results in retrieved performance and complexity.However, the existing hierarchical diffusion process methods only diffuse the affinity values in low-level visual space without considering the high-level semantic information, which cause the problem of semantic gap.To overcome these problems, we propose a Graph Regularized Hierarchical Diffusion Process (GRHDP) method with relevance feedback, and apply it to retrieve medical images.The proposed algorithm firstly establishes a hierarchical structure of the images in medical image database and spreads the affinity values among query images and top-layer images by graph regularization diffusion.Then relevance feedback is introduced to adjust the similarity between query images and retrieved images in top layer, and the affinity values are diffused again according to labeled information of feedback.Finally, the similarity between queries and others in database can be obtained by interpolating the diffused results on the top layer from top to bottom.The experimental results show that our proposed GRHDP with relevance feedback has achieved better retrieval performance than manifold ranking and regularized diffusion process (RDP) when returning top retrieved images.
Feature extraction of point cloud using 2D-3D transformation

The current 3D point cloud feature extraction algorithms are mostly based on geometric features of points. And the distribution of feature points is so messy to accurately locate. This paper proposes a point cloud feature extraction algorithm using 2D-3D transformation. By selecting three pairs of 2D image and 3D point cloud feature points, the conversion matrix of image and point cloud coordinates is calculated to establish a mapping relationship and then we realize the extraction of point cloud features. Experimental results show that compared with other algorithms, the algorithm proposed in this paper can extract the detailed features of point cloud more accurately.
Offline Model-Based Optimization via Normalized Maximum Likelihood  Estimation

In this work we consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process, such as in the design of materials, vehicles, or neural network architectures. Because the available data typically only covers a small manifold of the possible space of inputs, a principal challenge is to be able to construct algorithms that can reason about uncertainty and out-of-distribution values, since a naive optimizer can easily exploit an estimated model to return adversarial inputs. We propose to tackle this problem by leveraging the normalized maximum-likelihood (NML) estimator, which provides a principled approach to handling uncertainty and out-of-distribution inputs. While in the standard formulation NML is intractable, we propose a tractable approximation that allows us to scale our method to high-capacity neural network models. We demonstrate that our method can effectively optimize high-dimensional design problems in a variety of disciplines such as chemistry, biology, and materials engineering.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text-based policies in TextWorld (Cote et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER’s simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, and visual scene understanding). Code, data, and videos are available at: alfworld.github.io
High Degree of Freedom Hand Pose Tracking Using Limited Strain Sensing and Optical Training

The ability to track human operators' hand usage when working in production plants and factories is critically important for developing realistic digital factory simulators as well as manufacturing process control. We propose a proof-of-concept instrumented glove with only a few strain gage sensors and a microcontroller that continuously tracks and records the hand configuration during actual use. At the heart of our approach is a trainable system that can predict the fourteen joint angles in the hand using only a small set of strain sensors. First, ten strain gages are placed at various joints in the hand to optimize the sensor layout using the English letters in the American Sign Language (ASL) as a benchmark for assessment. Next, the best sensor configurations for three through ten strain gages are computed using a support vector machine (SVM) classifier. Following the layout optimization, our approach learns a mapping between the sensor readouts to the actual joint angles optically captured using a Leap Motion system. Five regression methods including linear, quadratic, and neural regression are then used to train the mapping between the strain gage data and the corresponding joint angles. The final proposed model involves four strain gages mapped to the fourteen joint angles using a two-layer feed-forward neural network (NN).
Sign Language Recognition Using Convolutional Neural Network

In today’s world, communication is very important. A language is needed to communicate. Most of the specially abled people, use a different language for communication called sign language. This language helps them to communicate with other people with their hand expressions. These expressions will be different from country to country. In this paper, American sign language is used. This paper deals with helping specially abled people to communicate with people who don’t know sign language by using the approaches of computer vision and deep learning. Our paper uses convolutional neural network to solve this problem. The first part of our paper focuses on capturing different hand expressions in the form of video by the person and translating them to text using a convolutional neural network. The other part focuses on the reverse of it, showing GIF upon converting text. Integrating these two parts will help in two-way communication.