Reviewerly/exHarmony huggingface dataset:
Self-supervised Video Object Segmentation by Motion Grouping

Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. We make the following contributions: First, we introduce a simple variant of the Transformer to segment optical flow frames into primary objects and the background. Second, we train the architecture in a self-supervised manner, i.e. without using any manual annotations. Third, we analyze several critical components of our method and conduct thorough ablation studies to validate their necessity. Fourth, we evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2, and FBMS59). Despite using only optical flow as input, our approach achieves superior or comparable results to previous state-of-the-art self-supervised methods, while being an order of magnitude faster. We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG) induced from existing text-to-SQL datasets. We pre-train our model on the synthetic data using a novel text-schema linking objective that predicts the syntactic role of a table field in the SQL for each question-SQL pair. To maintain the model's ability to represent real-world data, we also include masked language modeling (MLM) over several existing table-and-language datasets to regularize the pre-training process. On four popular fully supervised and weakly supervised table semantic parsing benchmarks, GraPPa significantly outperforms RoBERTa-large as the feature representation layers and establishes new state-of-the-art results on all of them.
Structured Hierarchical Dialogue Policy with Graph Neural Networks

Dialogue policy training for composite tasks, such as restaurant reservations in multiple places, is a practically essential and challenging problem. Recently, hierarchical deep reinforcement learning (HDRL) methods have achieved excellent performance in composite tasks. However, in vanilla HDRL, both top-level and low-level policies are all represented by multi-layer perceptrons (MLPs), which take the concatenation of all observations from the environment as the input for predicting actions. Thus, the traditional HDRL approach often suffers from low sampling efficiency and poor transferability. In this paper, we address these problems by utilizing the flexibility of graph neural networks (GNNs). A novel ComNet is proposed to model the structure of a hierarchical agent. The performance of ComNet is tested on composited tasks of the PyDial benchmark. Experiments show that ComNet outperforms vanilla HDRL systems with performance close to the upper bound. It not only achieves sample efficiency but also is more robust to noise while maintaining the transferability to other composite tasks.
Dual Convolutional Neural Networks for Breast Mass Segmentation and  Diagnosis in Mammography

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis (CAD) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL) focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the semantics and structure are well preserved and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. We evaluated our method on two most used public mammography datasets, DDSM and INbreast. Experimental results show that DualCoreNet achieves the best mammography segmentation and classification simultaneously, outperforming recent state-of-the-art models.
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance  Discrimination

Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods. In this paper, we propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. The key idea of our method is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HR-Net to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, the network is concatenated with simple task-specific back-end layers and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method and validate our network and loss design with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods, especially in tasks with a small labeled dataset. For fine-grained shape segmentation, our method even surpasses existing supervised methods by a large margin.
Compositional Generalization by Learning Analytical Expressions

Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily. However, existing neural network based models have been proven to be extremely deficient in such a capability. Inspired by work in cognition which argues compositionality can be captured by variable slots with symbolic functions, we present a refreshing view that connects a memory-augmented neural model with analytical expressions, to achieve compositional generalization. Our model consists of two cooperative neural modules, Composer and Solver, fitting well with the cognitive argument while being able to be trained in an end-to-end manner via a hierarchical reinforcement learning algorithm. Experiments on the well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization, solving all challenges addressed by previous works with 100% accuracies.
Self-Supervised Joint Learning Framework of Depth Estimation via  Implicit Cues

In self-supervised monocular depth estimation, the depth discontinuity and motion objects' artifacts are still challenging problems. Existing self-supervised methods usually utilize a single view to train the depth estimation network. Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects. In this work, we propose a novel self-supervised joint learning framework for depth estimation using consecutive frames from monocular and stereo videos. The main idea is using an implicit depth cue extractor which leverages dynamic and static cues to generate useful depth proposals. These cues can predict distinguishable motion contours and geometric scene structures. Furthermore, a new high-dimensional attention module is introduced to extract clear global transformation, which effectively suppresses uncertainty of local descriptors in high-dimensional space, resulting in a more reliable optimization in learning framework. Experiments demonstrate that the proposed framework outperforms the state-of-the-art(SOTA) on KITTI and Make3D datasets.
Back-stepping Nonlinear Integral Sliding Mode Control of a Vision Augmented 3 DoF Quadrotor in the presence of System Uncertainties and Bounded Disturbances

This work presents the development of a back-stepping nonlinear integral sliding mode controller (BSNISMC) for a 3 DoF vision augmented quadrotor, in the presence of system uncertainties and bounded external disturbances. Vision augmented models play a significant role in visual servoing by enabling the use of torque based commands to directly alter the position of the quadrotor with respect to the desired point of interest (POI), in the camera frame. Since the model, at hand, is a vision augmented model, the overall control heavily relies upon the availability of quadrotor and camera parameters, which, in most practical cases, cannot be determined accurately. In addition, camera noises as well as external disturbances hinder the performance of the system significantly. To overcome these issues, this work develops a back-stepping nonlinear integral sliding mode controller which is known to be robust against bounded external disturbances. The inclusion of the nonlinear integral sliding manifold ensures better steady state response of the system. To cater to the system uncertainties, adaptation law’s for these parameters are obtained using Lyapunov’s theory, which also aids in establishing the stability of the system. Numerical simulations are presented to demonstrate the efficacy of the controller and the adaptation laws.
AlexNet-based Visible light communication devices fingerprint extraction and authentication in broadcast systems

Visible Light Communication (VLC) is one of technology for the sixth generation (6G) wireless communication and also broadcast system. VLC systems are more resistant against Radio Frequency interference and unsusceptible to security like most RF wireless networks. Since VLC is one of suitable candidate for enforcing data security in future wireless networks. This paper considers improving the security of the next generation of wireless communications by using wireless device fingerprints in visible light communication, which could be used potentially for ATSC broadcasting applications. In particular, we aim to provide a detailed proposal for developing novel wireless security solutions using Visible light communication device fingerprinting techniques. The objectives are two-fold: (1) to provide a systematic review of AI-based wireless device fingerprint identification method and (2) to identify VLC transmitter, with respect to the ATSC physical layer modulation scheme, by analysing the differences in the modulated constellations signaled received by photo-diode, which will be proved by laboratory experimentation.
Analytical Reasoning of Text

Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.However, current neural models with implicit reasoning ability struggle to solve this task.In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.We analyze what knowledge understanding and reasoning abilities are required to do well on this task, and present an approach dubbed ARM.It extracts knowledge such as participants and facts from the context.Such knowledge are applied to an inference engine to deduce legitimate solutions for drawing conclusions.In our experiments, we find that ubiquitous pretrained models struggle to deal with this task as their performance is close to random guess.Results show that ARM outperforms pre-trained models significantly.Moreover, we demonstrate that ARM has better explicit interpretable reasoning ability.
On the Machine Learning Based Business Workflows Extracting Knowledge from Large Scale Graph Data

The data created by web users while navigating on a website constitutes graph data. Large-scale graph data is generated on websites many users visit with high frequency. Analyzing large-scale graph data using artificial intelligence techniques and predicting user behavior by creating models is an actively studied research topic. Within the scope of this research, a machine learning business process is proposed that will allow the interpretation of graph data obtained from web user navigation data. A prototype application was developed to demonstrate the usability of the proposed business process. The developed prototype application was run on graph data obtained from websites with intense user-system interaction. A comprehensive evaluation study was carried out on the prototype application. The results obtained from the empirical evaluation are promising and show that the proposed business process is used.
Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers

Large-scale surveys are a widely used instrument to collect data from a target audience.Beyond the single individual, an appropriate analysis of the answers can reveal trends and patterns and thus generate new insights and knowledge for researchers.Current analysis practices employ shallow machine learning methods or rely on (biased) human judgment.This work investigates the usage of state-of-the-art NLP models such as BERT to automatically extract information from both open-and closed-ended questions.We also leverage explainability methods at different levels of granularity to further derive knowledge from the analysis model.Experiments on EMS-a survey-based study researching influencing factors affecting a student's career goals-show that the proposed approach can identify such factors both at the input-and higher concept-level.
Improving Neural Abstractive Summarization with Reliable Sentence Sampling

State-of-the-art abstractive summarization models are able to produce summaries for various types of sources with quality comparable to human written texts. However, despite the fluency, the generated summaries are often erroneous due to factual inconsistencies caused by neural hallucinations. In this work, we study possible ways of reducing the hallucination rate during abstractive summarization. We compare three different techniques aimed at improving the correctness of the training procedure: control tokens, truncated loss, and dataset cleaning. To control hallucination rate outside of the training, we propose an improved algorithm for summary sampling - reliable sentence sampling. The algorithm utilizes fact precision metrics to sample the most reliable sentences for an abstractive summary. By conducting the human evaluation, we demonstrate the algorithm’s efficiency in preserving summary factual consistency.
Smart Anomaly Detection and Monitoring of Industry 4.0 by Drones

Nowadays, industry 4.0 can be distributed over a large area. To monitor their processes, they use sensors that periodically gather data on the system. Based on them, operators can detect that an anomaly occurs on the system. However, it is not always easy to know the causes of the anomaly because the operator has no visual information on the system. To help operators to identify the root of the anomaly, drones are very useful because they are fast enough to intervene in large-scale industry and embed a large variety of sensors to offer complementary data (images, video…) that are necessary for the diagnosis. However, drones have to be synchronized with the industrial process to know where the anomaly occurs and to go there in an automated way. We propose a new architecture to automate the displacement of the drone to reach safely the place where the anomaly is located and to confirm it using a deep-learning approach. The drone embeds a small computing system (Raspberry Pi) which communicates with the supervisory control and data acquisition system in order to be aware of anomalies that occur on the industrial process. To function properly indoor or outdoor, the drone is positioned either using a precise positioning system based on ultra-wide band (UWB) or on the GPS. The drone can take pictures of the potentially detected anomaly and thanks to a neural network algorithm, it analyzes the images to confirm or deny the anomaly. The results show an error on the indoor position of about 5 cm, and a precision of about 90% to detect anomalies.
Influence of a non-linear interaction and Kerr medium on the emission spectrum and entropy squeezing of Su(1,1) algebra with single qubit

The interaction of a quantum algebraic system su(1,1) interacting with a qubit is studied. A nonlinear function has been added to interaction with the presence of Kerr medium (KM). The general solution is obtained using the constants of motion and Heisenberg equation for the time-evolution operator. The effects of the nonlinear function and KM on the emission spectrum and entropy squeezing are calculated. The results showed that the emission spectrum is affected by the nonlinear function. Symmetric peaks are generated around the vertical axis with increasing influence of the nonlinear function. By activating the role of the KM, the emission spectrum loses symmetry around the vertical axis and the maximum values decrease. Moreover, the KM reduces the squeezing intervals, while the squeezing intervals are improved by increasing the effect of the nonlinear function.
Coordinated Path Planning through Local Search and Simulated Annealing

The third computational geometry challenge was on a coordinated path planning problem in which a collection of square robots need to move on the integer grid, from their given starting points to their target points, and without collision between robots, or between robots and a set of input obstacles. We designed and implemented three algorithms for this problem. First, we computed a feasible solution by placing middle-points outside of the minimum bounding box of the starting positions, the target positions and the obstacles, and moving each robot from its starting point to its target point through a middle-point. Second, we applied a simple local search approach where we repeatedly delete and insert again a random robot through an optimal path. It improves the quality of the solution, as the robots no longer need to go through the middle-points. Finally, we used simulated annealing to further improve this feasible solution. We used two different types of moves: We either tightened the whole trajectory of a robot, or we stretched it between two points by making the robot move through a third intermediate point generated at random.
The Implementation of Genre-Based Pedagogy with Technology in EFL Classroom

The implementation of genre-based pedagogy in the education context has been proven to improve student's literacy skills, especially writing skills.However, along with the development of technology, the EFL classrooms have integrated technology as a tool for teachers and students to achieve learning goals.This study attempts to investigate the re-contextualization of genre-based pedagogy in the technology-integrated classroom.This study follows a qualitative research design that focused on phenomenology research design in order to explore the teachers' experience in implementing the genre-based pedagogy with technology in teaching writing.This study involved two English teachers from Junior High School in Bandung, West Java.The data was obtained from the in-depth interviews and then analysed qualitatively by transcribing the data interview and drawing conclusions.The result of this study showed that teachers have conducted the stages of genre-based pedagogy properly with technology-assisted.The use of technology tools in teaching writing can make students develop their background knowledge about some texts in a particular genre and also constructed purposeful and creative writing.Therefore, the implementation of genre-based pedagogy with technology in teaching writing has an important role to increase students' literacy skills.
Efficient privacy-preserving k-nearest neighbors in cloud computing

In the face of the increasing volume of data stored today, various enterprise organization outsources their databases to cloud server vendors for storage and management in order on database management costs. As enterprise organization encrypts their data for security reasons when uploading, this can create certain problems for the usability of the data; it is necessary to implement a kNN solution that supports secure queries over ciphertext. A number of approaches on its privacy protection have emerged, but there are still issues such as efficiency and security. To address these issues, an efficient and secure k-neighborhood query scheme SEkNN (Secure and efficient kNN) over cloud computing was proposed using garbed circuits combining secret sharing and Yao protocol to construct Euclidean computation as well as a secure ordering algorithm that works completely secure and more efficient. Security analysis was also performed to prove that the scheme was secure under the semi-honest model. The final experiment was compared with a lightweight kNN scheme based on edge computing (EBkNN) and verified that the scheme better meets the needs of practical application.
The Stack as an Integrative Model of Global Capitalism

This paper investigates recent transformations in global capitalism’s political economy, concerned with the evolution of globally integrated production and exchange apparatuses, such as platforms, enabled through technological advances in computational infrastructures. These infrastructures are explicable in terms of the model of the Stack, understood as an accidental mega-structure of the contemporary platform economy that is integrating previously detached circulation and accumulation structures. The Stack is introduced as an integrative model of a multi-layered political economic system that allows to understand and explain recent developments in global capitalism. Focus is thereby given to intensified real abstraction of labour induced by the capitalist appropriation of planetary-scale computation. Building on the model of the Stack, we set in relation different perspectives on recent capitalist development in terms of planetary-scale computation: transnational informational capitalism, cognitive capitalism, intellectual monopoly capitalism and techno-feudalism. Thereby we highlight aspects of value creation as well as rent-seeking through the model of the Stack.