Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Task-Parameterized Movement Primitives

Programming by demonstrations has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized movement learning has been recently developed [4], which has achieved reliable performance in areas such as human-robot collaboration and robot bimanual operation. However, the crucial task frames and associated task parameters in this learning framework are often set based on human experience, which renders three problems that have not been addressed yet: (i) task frames are treated equally without considering the task priorities; (ii) task parameters are defined without considering additional task constraints, e.g., robot joint limits and motion smoothness; (iii) a fixed number of task frames are pre-defined regardless some of them are redundant or even irrelevant for the task at hand. In this paper, we generalize the task-parameterized learning by addressing the aforementioned problems. Moreover, we provide an alternative way to refine and adapt previously learned robot skills, which allows us to work on a low dimensional space. Several examples are studied in simulated and real robotic systems, showing the applicability of our approach.
Recursive Cross-Domain Face/Sketch Generation from Limited Facial Parts

We start by asking an interesting yet challenging question,"If an eyewitness can only recall the eye features of the suspect, such that the forensic artist can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig. 1), can advanced computer vision techniques help generate the whole face image? "A more generalized question is that if a large proportion (e.g., more than 50%) of the face/sketch is missing, can a realistic whole face sketch/image still be estimated. Existing face completion and generation methods either do not conduct domain transfer learning or can not handle large missing area. For example, the inpainting approach tends to blur the generated region when the missing area is large (i.e., more than 50%). In this paper, we exploit the potential of deep learning networks in filling large missing region (e.g., as high as 95% missing) and generating realistic faces with high-fidelity in cross domains. We propose the recursive generation by bidirectional transformation networks (r-BTN) that recursively generates a whole face/sketch from a small sketch/face patch. The large missing area and the cross domain challenge make it difficult to generate satisfactory results using a unidirectional cross-domain learning structure. On the other hand, a forward and backward bidirectional learning between the face and sketch domains would enable recursive estimation of the missing region in an incremental manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial constraint to encourage the generation of realistic faces/sketches. Extensive experiments have been conducted to demonstrate the superior performance from r-BTN as compared to existing potential solutions.
Distributed Convolutional Sparse Coding

We consider the problem of building shift-invariant representations for long signals in the context of distributed processing. We propose an asynchronous algorithm based on coordinate descent called DICOD to efficiently solve the $\ell_1$-minimization problems involved in convolutional sparse coding. This algorithm leverages the weak temporal dependency of the convolution to reduce the interprocess communication to a few local messages. We prove that this algorithm converges to the optimal solution and that it scales with superlinear speedup, up to a certain limit. These properties are illustrated with numerical experiments and our algorithm is compared to the state-of-the-art methods used for convolutional sparse coding.
A First-Order Functional Intermediate Language for Verified Compilers

We present the rst-order intermediate language IL for veried compilers. IL is a functional language with calls to a nondeterministic environment. We give IL terms a second, imperative semantic interpretation and obtain a register transfer language. For the imperative interpretation we establish a notion of live variables. Based on live variables, we formulate a decidable property called coherence ensuring that the functional and the imperative interpretation of a term coincide. We formulate a SSA-based register assignment algorithm for IL and prove its correctness. The algorithm translates a functional IL program into an equivalent imperative IL program. Correctness follows from the fact that the algorithm reaches a coherent program after consistently renaming local variables. We prove that the maximal number of live variables in the initial program bounds the number of dierent variables in the nal coherent program. The entire development is formalized in Coq.
The role of outcome feedback in improving the uncertainty assessment of software development effort estimates

Previous studies report that software developers are over-confident in the accuracy of their effort estimates. Aim: This study investigates the role of outcome feedback, that is, feedback about the discrepancy between the estimated and the actual effort, in improving the uncertainty assessments. Method: We conducted two in-depth empirical studies on uncertainty assessment learning. Study 1 included five student developers and Study 2, 10 software professionals. In each study the developers repeatedly assessed the uncertainty of their effort estimates of a programming task, solved the task, and received estimation accuracy outcome feedback. Results: We found that most, but not all, developers were initially over-confident in the accuracy of their effort estimates and remained over-confident in spite of repeated and timely outcome feedback. One important, but not sufficient, condition for improvement based on outcome feedback seems to be the use of explicitly formulated, instead of purely intuition-based, uncertainty assessment strategies.
Learning Competing Constraints and Task Priorities from Demonstrations of Bimanual Skills

As bimanual robots become increasingly popular, learning and control algorithms must take into account new constraints and challenges imposed by this morphology. Most research on learning bimanual skills has focused on learning coordination between end-effectors, exploiting operational space formulations. However, motion patterns in bimanual scenarios are not exclusive to operational space, also occurring at the joint level. Moreover, bimanual operation offers the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization in bimanual settings. Here we address the aforementioned problems from a robot learning perspective. We go beyond operational space and present a principled approach to simultaneously learn operational and configuration space constraints, as well as the evolution of task priorities from demonstrations. Our method extends the Task-Parameterized Gaussian Mixture Model (TP-GMM) to the use of projection operators which allow for tackling such problems. The approach is validated in two different bimanual tasks with the COMAN and WALK-MAN humanoids that either require the consideration of constraints in both operational and configuration spaces, or the prioritization of tasks.
Unsupervised body part regression using convolutional neural network with self-organization

Automatic body part recognition for CT slices can benefit various medical image applications. Existing methods require large-scale datasets of labeled images. However, the intrinsic structure information in CT volumes was still not fully leveraged. In this paper, we propose unsupervised body part regressor (UBR) to utilize this information. A novel learning framework based on a convolutional neural network and two inter-sample loss functions was designed. UBR builds a coordinate system for the body and outputs a continuous score for each slice, which represents the normalized position of the body part in the slice. The training process of UBR resembles a self-organization process: slice scores are learned from inter-slice relationships. The training samples are unlabeled volumes that are abundant in every hospital's database, thus zero annotation effort is needed. UBR is simple, fast, and accurate. Qualitative and quantitative experiments proved its effectiveness. Besides, we show potential applications of UBR in network initialization and anomaly detection.
The use of baseline measures in risk assessment

In the financial sector, risk management has become a well established method for ensuring that investments are made on a sound basis. Risk management of protective security is followed by many countries and organisations. Risk management promotes effective and appropriate methods of security. It directly correlates an asset's vulnerabilities to perceived risks rather than relying on prescribed “across the board” “physical security standards”. Whilst the principle of risk assessment is not in question, there may be some shortcomings as the result of the way it is implemented. Implementation of risk management touches all levels of the management chain. The success of risk management is as good as the practises that implement it. The management of risk must not be clouded by perceived conditions or agendas. The UK Government has approached the issues above through the incorporation of “baseline measures” within the risk management process. These measures are designed to ensure that the same level of protection is afforded to classified information and yet at the same time encourage security managers to use a risk management approach. It is designed to ensure that managers have considerable discretion as to the way in which protection is provided, while ensuring the agreed level of protection is achieved. Baseline measures apply to all government departments handling protectively marked, (“Classified”) material. The Ministry of Defence has extended these measures to encompass the protection of large items of classified equipment
TARDIS: Timestamp based Coherence Algorithm for Distributed Shared Memory

A new memory coherence protocol, Tardis, is proposed. Tardis uses timestamp counters representing logical time as well as physical time to order memory operations and enforce sequential consistency in any type of shared memory system. Tardis is unique in that as compared to the widely-adopted directory coherence protocol, and its variants, it completely avoids multicasting and only requires O(log N) storage per cache block for an N-core system rather than O(N) sharer information. Tardis is simpler and easier to reason about, yet achieves similar performance to directory protocols on a wide range of benchmarks run on 16, 64 and 256 cores.
From MEGATON to RASCAL: Surfing the Parameter Space of Evolutionary Algorithms

The practice of evolutionary algorithms involves a mundane yet inescapable phase, namely, finding parameters that work well. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? All these nagging questions need good answers if one is to embrace success. Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters. We aver that this renders the life of the practitioner that much easier, and cap off our study with an advisory digest for the weary.
Comprehensive Functional Verification: The Complete Industry Cycle

One of the biggest challenges in chip and system design is determining whether the hardware works correctly. That is the job of functional verification engineers and they are the audience for this comprehensive text from three top industry professionals. As designs increase in complexity, so has the value of verification engineers within the hardware design team. In fact, the need for skilled verification engineers has grown dramatically--functional verification now consumes between 40 and 70% of a project's labor, and about half its cost. Currently there are very few books on verification for engineers, and none that cover the subject as comprehensively as this text. A key strength of this book is that it describes the entire verification cycle and details each stage. The organization of the book follows the cycle, demonstrating how functional verification engages all aspects of the overall design effort and how individual cycle stages relate to the larger design process. Throughout the text, the authors leverage their 35 plus years experience in functional verification, providing examples and case studies, and focusing on the skills, methods, and tools needed to complete each verification task. Additionally, the major vendors (Mentor Graphics, Cadence Design Systems, Verisity, and Synopsys) have implemented key examples from the text and made these available on line, so that the reader can test out the methods described in the text. * Comprehensive overview of the complete verification cycle * Combines industry experience with a strong emphasis on functional verification fundamentals * Includes real-world case studies and downloadable software implementations of key examples from the major vendors (Mentor Graphics, Cadence Design Systems, Verisity, and Synopsys) Table of Contents Part I: Introduction to Verification Chapter 1: Verification in the Chip Design Process Chapter 2: Verification Flow Chapter 3: Fundamentals of Simulation Based Verification Chapter 4: The Verification Plan Part II: Simulation-Based Verification Chapter 5: HDLs and Simulation Engines Chapter 6: Creating Environments Chapter 7: Strategies for Simulation-based Stimulus Generation Chapter 8: Strategies for Results Checking in Chapter 9: Pervasive Function Verification Chapter 10: Re-Use Strategies and System Simulation Part III: Formal Verification Chapter 11 Introduction to Formal Verification Chapter 12 Using Formal Verification Part IV: Comprehensive Verification Chapter 13: Completing the Verification Cycle Chapter 14: Advanced Verification Techniques Part V: Case Studies Chapter 15: Case Studies Glossary References
Generative Autotransporters

In this paper, we aim to introduce the classic Optimal Transport theory to enhance deep generative probabilistic modeling. For this purpose, we design a Generative Autotransporter (GAT) model with explicit distribution optimal transport. Particularly, the GAT model owns a deep distribution transporter to transfer the target distribution to a specific prior probability distribution, which enables a regular decoder to generate target samples from the input data that follows the transported prior distribution. With such a design, the GAT model can be stably trained to generate novel data by merely using a very simple $l_1$ reconstruction loss function with a generalized manifold-based Adam training algorithm. The experiments on two standard benchmarks demonstrate its strong generation ability.
Feature-based or Direct: An Evaluation of Monocular Visual Odometry

Monocular visual odometry (VO), which incrementally estimates camera poses and local 3D maps, is the key component of monocular simultaneously localization and mapping (SLAM). The two main branches of the VO family, namely the feature-based and the so called direct methods, have seen tremendous improvements on accuracy, robustness and efficiency, and have gained exponential popularity over recent years. Nevertheless, no comprehensive evaluation between them has been performed. In this paper, we compare the state-of-the-art feature-based and direct VO methods, aiming at an exhaustive evaluation under diverse real-world settings. For the first time, we evaluate their performance on images with and without photometric calibration, camera motion bias and robustness to rolling shutter effect. We discuss about the possible reasons and give explanations to all our experiment results.
Unifacta: Profiling-driven String Pattern Standardization

Data cleaning is critical for effective data analytics on many real-world data collected. One of the most challenging data cleaning tasks is pattern standardization-reformatting ad hoc data, e.g., phone numbers, human names and addresses, in heterogeneous non-standard patterns (formats) into a standard pattern-as it is tedious and effort-consuming, especially for large data sets with diverse patterns. In this paper, we develop Unifacta, a technique that helps the end user standardize ill-formatted ad hoc data. With minimum user input, our proposed technique can effectively and efficiently help the end user synthesize high quality explainable pattern standardization programs. We implemented Unifacta, on Trifacta, and experimentally compared Unifacta with a previous state-of-the-art string transformation tool, Flashfill, along with Trifacta and Blinkfill. Experimental results show that Unifacta produced programs of comparable quality, but more explainable, while requiring substantially less user effort than Flashfill, and other related baseline systems. In a user effort study, Unifacta saved 30\% - 70\% user effort compared to the baseline systems. In an experiment testing the user's understanding of the synthesized transformation logic, Unifacta users achieved a success rate about twice that of Flashfill users.
Agent-based Learning for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure a CAV will have access to large amounts of useful data. This paper investigates a data driven driving policy learning framework through an agent based learning. A reinforcement learning framework is presented in the paper, which simulates the self-evolution of a CAV over its lifetime. The results indicated that overtime the CAVs are able to learn useful policies to avoid crashes and achieve its objectives in more efficient ways. Vehicle to vehicle communication in particular, enables additional useful information to be acquired by CAVs, which in turn enables CAVs to learn driving policies more efficiently. The simulation results indicate that while a CAV can learn to make autonomous decision V2V communication of information improves this capability. The future work will investigate complex driving policies such as roundabout negotiations, cooperative learning between CAVs and deep reinforcement learning to traverse larger state spaces.
A content engagement score for online learning platforms

Engagement on online learning platforms is essential for user retention, learning, and performance. However, there is a paucity of research addressing latent engagement measurement using user activities. In this work in progress paper, we present a novel engagement score consisting of three sub-dimensions - cognitive engagement, emotional engagement, and behavioral engagement using a comprehensive set of user activities. We plan to evaluate our score on a large scale online learning platform and compare our score with measurements from a user survey-based engagement scale from the literature.
Reinforcement Learning Based Conversational Search Assistant

In this work, we develop an end-to-end Reinforcement Learning based architecture for a conversational search agent to assist users in searching on an e-commerce marketplace for digital assets. Our approach caters to a search task fundamentally different from the ones which have limited search modalities where the user can express his preferences objectively. The system interacts with the users to display search results to the queries, and gauges user's intent and context of the conversation to choose the next action and reply. To train the agent in the absence of true conversation data, a virtual user is constructed to model a human user using the query and session logs from a major stock photography and digital assets marketplace. The system provides an alternative that is more engaging than the traditional search while maintaining similar effectiveness. This work provides a mechanism to build and deploy bootstrapped version of an effective conversational agent from readily available query log data. The system can then be used to acquire true conversational data and be fine-tuned further. The methodology discussed in this paper can be extended to e-commerce domains in general.
Dynamic cohesion measurement for distributed system

Instead of developing single-server software system for the powerful computers, the software is turning from large single-server to multi-server system such as distributed system. This change introduces a new challenge for the software quality measurement, since the current software analysis methods for single-server software could not observe and assess the correlation among the components on different nodes. In this paper, a new dynamic cohesion approach is proposed for distributed system. We extend Calling Network model for distributed system by differentiating methods of components deployed on different nodes. Two new cohesion metrics are proposed to describe the correlation at component level, by extending the cohesion metric of single-server software system. The experiments, conducted on a distributed systems-Netflix RSS Reader, present how to trace the various system functions accomplished on three nodes, how to abstract dynamic behaviors using our model among different nodes and how to evaluate the software cohesion on distributed system.
Parallel Weight Consolidation: A Brain Segmentation Case Study

Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case that derivative datasets or predictive models developed within individual sites can be shared and combined with fewer restrictions. Training on distributed datasets and combining the resulting networks is often viewed as continual learning, but these methods require networks to be trained sequentially. In this paper, we introduce parallel weight consolidation (PWC), a continual learning method to consolidate the weights of neural networks trained in parallel on independent datasets. We perform a brain segmentation case study using PWC to consolidate several dilated convolutional neural networks trained in parallel on independent structural magnetic resonance imaging (sMRI) datasets from different sites. We found that PWC led to increased performance on held-out test sets from the different sites, as well as on a very large and completely independent multi-site dataset. This demonstrates the feasibility of PWC for combining the knowledge learned by networks trained on different datasets.