Reviewerly/exHarmony huggingface dataset:
Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models

Code completion is widely used by software developers to provide coding suggestions given a partially written code snippet. Apart from the traditional code completion methods, which only support single token completion at minimal positions, recent studies show the ability to provide longer code completion at more flexible positions. However, such frequently triggered and longer completion results reduce the overall precision as they generate more invalid results. Moreover, different studies are mostly incompatible with each other. Thus, it is vital to develop an ensemble framework that can combine results from multiple models to draw merits and offset defects of each model. This paper conducts a coding simulation to collect data from code context and different code completion models and then apply the data in two tasks. First, we introduce an acceptance model which can dynamically control whether to display completion results to the developer. It uses simulation features to predict whether correct results exist in the output of these models. Our best model reduces the percentage of false-positive completion from 55.09% to 17.44%. Second, we design a fusion ranking scheme that can automatically identify the priority of the completion results and reorder the candidates from multiple code completion models. This scheme is flexible in dealing with various models, regardless of the type or the length of their completion results. We integrate this ranking scheme with two frequency models and a GPT-2 styled language model, along with the acceptance model to yield 27.80% and 37.64% increase in TOP1 and TOP5 accuracy, respectively. In addition, we propose a new code completion evaluation metric, Benefit-Cost Ratio(BCR), taking into account the benefit of keystrokes saving and hidden cost of completion list browsing, which is closer to real coder experience scenario.
A streaming hardware architecture for real-time SIFT feature extraction

The Scale-Invariant Feature Transform (SIFT) is a feature extractor that serves as a key step in many computer-vision pipelines. Real-time operation based on a software-only approach is often infeasible, but FPGAs can be employed to parallelize execution and accelerate the application to meet latency requirements. In this study, we present a stream-based hardware acceleration architecture for SIFT feature extraction. Using a novel strategy to store pixels required for descriptor computation, the execution time needed to generate SIFT descriptors is greatly improved relative to previous designs. This strategy also enables further reduction of the execution time by introducing multiple processing elements (PEs) for computation of several SIFT descriptors in parallel. Additionally, the proposed architecture supports keypoint detection at an arbitrary number of octaves and allows for runtime configuration of various parameters. An FPGA implementation targeting the Xilinx Zynq-7045 system-on-chip (SoC) device is deployed to demonstrate the efficiency of the proposed architecture. In the target hardware, the resulting system is capable of processing images with a resolution of 1280 × 720 pixels at up to 150 FPS while maintaining modest resource utilization.
Compressive imaging for thwarting adversarial attacks on 3D point cloud classifiers

Three dimensional (3D) point cloud classifiers are used extensively in safety crucial applications such as autonomous cars, face recognition, military applications, and many more. Despite the critical importance of their reliability, 3D classifiers are prone to adversarial attacks that can be crafted in the real world. While it is possible to use known methods to prevent adversarial attacks, they can be easily counter-attacked, leading to an arms race between the attacker and the defender. Here, we propose to use 3D compressive sensing to recover an original label of the 3D object. Since compressive sensing inherently encodes the 3D signal, it also prevents the arms race between the attacker and the defender. The 3D compressive sensing we consider is a single pixel camera (SPC) system that can be implemented in Light Detection and Ranging (LiDAR) systems.
Performance based Risk driven Trust (PRTrust): On modeling of secured service sharing in peer-to-peer federated cloud

Peer-to-peer (P2P) federated cloud system has emerged as a promising service delivery model wherein multiple cloud service providers (CSPs) collaborate and share resources and services among themselves to fulfill the spikes in demand from their cloud service consumers (CSCs). It facilitates CSPs to accomplish the committed service level agreements (SLAs) to their CSCs. However, the lack of preexisting trust relationships and unawareness about the cloud infrastructure and service delivery performance among CSPs in a distributed environment poses a risk to the quality of service (QoS) being delivered. We address this challenge by proposing a trust model, PRTrust, for a peer-to-peer federated cloud system that capitalizes the triangular relationship of performance, risk, and trust for the participating CSPs. The main contributions of this work are: (1) providing a logical design and architecture for trust and performance management in a peer-to-peer federated cloud system; (2) providing a two-tier weighted performance evaluation mechanism for CSPs; (3) providing a risk evaluation mechanism for CSPs based on their current performance level; (4) providing an improvised mechanism to evaluate and manage personalized reputation-based trust for the participating CSPs; (5) providing a CSP selection mechanism from a trusted list of CSPs, using the evaluated performance-based risk, for sharing the resources and services in a peer-to-peer federated cloud system. The proposed PRTrust model has shown better threat resilient behavior in dealing with malicious peer CSPs when compared with the reference EigenTrust model.
VR Rehearse &amp; Perform - A platform for rehearsing in Virtual Reality

In this paper, we propose VR Rehearse & Perform - a Virtual Reality application for enhancing the rehearsal efforts of performers by providing them access to accurate recreations - both visual and acoustical - of iconic concert venues.
Kinodynamic planning for an energy-efficient autonomous ornithopter

This paper presents a novel algorithm to plan energy-efficient trajectories for autonomous ornithopters. In general, trajectory optimization is quite a relevant problem for practical applications with Unmanned Aerial Vehicles (UAVs). Even though the problem has been well studied for fixed and rotatory-wing vehicles, there are far fewer works exploring it for flapping-wing UAVs, like ornithopters. These are of interest for many applications where long-flight endurance, but also hovering capabilities, are required. We propose an efficient approach to plan ornithopter trajectories that minimize energy consumption by combining gliding and flapping maneuvers. Our algorithm builds a tree of dynamically feasible trajectories and it applies heuristic search for efficient online planning, using reference curves to guide the search and prune states. We present computational experiments to analyze and tune the key parameters, as well as a comparison against a recent alternative probabilistic planner, showing best performance. Finally, we demonstrate how our algorithm can be used for planning perching maneuvers online.
Pedestrian Occlusion Level Classification using Keypoint Detection and 2D Body Surface Area Estimation

Effective and reliable pedestrian detection is among the most safety-critical features of semi-autonomous and autonomous vehicles. One of the most complex detection challenges is that of partial occlusion, where a target object is only partially available to the sensor due to obstruction by another foreground object. A number of current pedestrian detection benchmarks provide annotation for partial occlusion to assess algorithm performance in these scenarios, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. In addition, current occlusion level annotation methods contain a high degree of subjectivity by the human annotator. This can lead to inaccurate or inconsistent reporting of an algorithm’s detection performance for partially occluded pedestrians, depending on which benchmark is used. This research presents a novel, objective method for pedestrian occlusion level classification for ground truth annotation. Occlusion level classification is achieved through the identification of visible pedestrian keypoints and through the use of a novel, effective method of 2D body surface area estimation. Experimental results demonstrate that the proposed method reflects the pixel-wise occlusion level of pedestrians in images and is effective for all forms of occlusion, including challenging edge cases such as self-occlusion, truncation and inter-occluding pedestrians.
Certificateless ring signcryption for multi-source network coding

Network coding can make the intermediate nodes encode the messages, and it has the merits of large network throughput, high resource utilization rate and strong system robustness. Traditional certificateless ring signcryption cannot be used in the network coding environment, so we construct one certificateless ring signcryption for multi-source network coding (MSNCCLRSC), in which actual signcrypter can anonymously signcrypt a message on behalf of a group of users including himself. MSNCCLRSC is provably confidential and unforgeable under the hardness of the co-decisional bilinear Diffie-Hellman and co-computational Diffie-Hellman problems in the random oracle model; moreover, it can resist the pollution attacks and has relatively low computation complexity.
High-fidelity 3D face reconstruction with multi-scale details

Despite tremendous success has been achieved in faithfully reconstructing face shapes from single images, recovering accurate local details still remains challenging. Previous works propose reprojection-based methods to improve the performance of detail recovering – they render a textured 3D shape into an image and make it approximate to the input during iterations. However, details from textures and shapes are mixed in the rendered image when minimizing the re-projection loss, which leads to limitations in detail recovery. To address this issue, we propose a novel 3D face reconstruction framework that 1) uses a coarse-medium-fine strategy to capture details while preserving the global shape, 2) disentangles details from the texture to enhance local accuracy, and 3) applies a phased optimization to recover details over multiple scales. Experiments demonstrate the capability of our framework to reconstruct high-fidelity face shapes with accurate, fine details.
Quantum Optimization Heuristics with an Application to Knapsack Problems

This paper introduces two techniques that make the standard Quantum Approximate Optimization Algorithm (QAOA) more suitable for constrained optimization problems. The first technique describes how to use the outcome of a prior greedy classical algorithm to define an initial quantum state and mixing operation to adjust the quantum optimization algorithm to explore the possible answers around this initial greedy solution. The second technique is used to nudge the quantum exploration to avoid the local minima around the greedy solutions. To analyze the benefits of these two techniques we run the quantum algorithm on known hard instances of the Knapsack Problem using unit depth quantum circuits. The results show that the adjusted quantum optimization heuristics typically perform better than various classical heuristics.
The TriLS Approach for Drift-Aware Time-Series Prediction in IIoT Environment

This article presents a novel drift-aware approach to multivariate time-series modeling in the nonstationary industrial Internet of Things environments. The three-layered three-state (TriLS) system enables cooperation between the gateway and the cloud toward the timely adjustment of a lightweight predictive model. Concept drift is detected by the cloud with the use of the extended adaptive windowing algorithm that operates on statistics of time sequences tracked by the gateway. This system is geared toward providing accurate predictions of nonstationary industrial processes for intelligent factory automation and safety. The proposed TriLS system is evaluated on records of recurring chemical processes collected at two plants and implemented on a Raspberry Pi board. TriLS achieves a lower prediction error than the reference adaptive schemes while reducing the computational effort and memory requirements for adaptation at the gateway by over 66% and 48%, respectively. It also reduces the volume of shared data between the gateway and the cloud by 40% –72% that is a significant cut on communications overhead.
Plots to Previews: Towards Automatic Movie Preview Retrieval using Publicly Available Meta-data

‘Preview’, a concept popularized by Netflix, is a contiguous scene of a movie or a TV show highlighting its story, characters, and tone, thus helping viewers to make quick viewing decisions. To create previews, one needs scenelevel semantic annotations related to the story, characters, and tone. Soliciting such annotations is an involved exercise and these are expensive to generate automatically. Instead, we aim at creating previews by availing readily available scene meta-data, while avoiding dependency on semantic scene-level annotations. We hypothesize that movie scenes that best match publicly available IMDb plot summaries can make good previews. We use 51 movies from the MovieGraph dataset, and find that a match of the plot summaries with scene dialogues, available through subtitles, is adequate to create usable movie previews, without the need for other semantic annotations. We validate the hypothesis by comparing ratings for scenes selected by the proposed method to those for scenes selected randomly, obtained from regular viewers as well as an expert. We report that even with this ‘minimalist’ approach, we can select at least one good preview scene for 26 out of 51 movies, as agreed upon by a critical expert judgment. Error analysis of the scenes indicates that features related to the plot structure might be needed to further improve the results.
Using Isabelle in Two Courses on Logic and Automated Reasoning

We present our experiences teaching two courses on formal methods and detail the contents of the courses and their positioning in the curriculum. The first course is a bachelor course on logical systems and logic programming, with a focus on classical first-order logic and automatic theorem proving. The second course is a master course on automated reasoning, with a focus on classical higher-order logic and interactive theorem proving. The proof assistant Isabelle is used in both courses, using Isabelle/Pure as well as Isabelle/HOL. We describe our online tools to be used with Isabelle/HOL, in particular the Sequent Calculus Verifier (SeCaV) and the Natural Deduction Assistant (NaDeA). We also describe our innovative Students' Proof Assistant which is formally verified in Isabelle/HOL and integrated in Isabelle/jEdit using Isabelle/ML.
“Vibrant Wearables”: Material Encounters with the Body as a Soft System

As new materials become available for textile and interaction designers, it is crucial that we develop an understanding of the lived experiences of such materials and explore meaningful contexts for their development. In this paper, we engage with systems in which bodies as materials and materials as bodies constitute an assemblage of vitalities in constant flux with one another. In particular, we address how such systems in their interactions with (non)human bodies blur boundaries between inside and outside the body, and between human and machine, acting as soft systems. Drawing on our first-person, design-led research, we present three design explorations of soft systems that deeply engage with the body: Breathing Wings, Fiddling Necklaces and Menarche Bits. We analyze how the three projects contribute towards what we conceptualize as “vibrant wearables”: wearables that through their material vibrancy surface design qualities of leakiness, characterized by a multi-directionality of “spilling over,” ongoingness, which attends to non-linear temporalities and cycles of life and death, and mutuality that emphasizes the interdependency, and becoming, of vibrant encounters. These three design qualities all conceptually trouble boundaries of bodies and materials and are practical resources for designers and researchers working with the body in/as a soft system. Our work offers concrete examples of how to work with material vibrancy, which is particularly relevant to new materialist discourses in textile, fashion and interaction design. We argue for the generativity of these design qualities for other designers and researchers aiming to elevate materials and soft systems in interactions with bodies. Moreover, we contribute towards design research that conceptually and materially troubles the boundaries of the body, and we argue for attending to the material power of (non)human bodies as a soft system.
Weight-of-evidence through shrinkage and spline binning for interpretable nonlinear classification

In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of classes are required to be both precise and interpretable. Linear modeling methods such as logistic regression are often adopted since they offer an acceptable balance between precision and interpretability. Linear methods, however, are not well equipped to handle categorical predictors with high cardinality or to exploit nonlinear relations in the data. As a solution, data preprocessing methods such as weight of evidence are typically used for transforming the predictors. The binning procedure that underlies the weight-of-evidence approach, however, has been little researched and typically relies on ad hoc or expert-driven procedures. The objective in this paper, therefore, is to propose a formalized, data-driven and powerful method. To this end, we explore the discretization of continuous variables through the binning of spline functions, which allows for capturing nonlinear effects in predictor variables and yields highly interpretable predictors that take only a small number of discrete values. Moreover, we extend the weight-of-evidence approach and propose to estimate the proportions using shrinkage estimators. Together, this method offers an improved ability to exploit both nonlinear and categorical predictors to achieve increased classification precision while maintaining the interpretability of the resulting model and decreasing the risk of overfitting. We present the results of a series of experiments in fraud detection and credit risk settings, which illustrate the effectiveness of the presented approach.
Cognex 2D Camera Calibration as 6-axis Robot Tool Automation

In the present work, an algorithm for calibrating a 2D camera as a tool for a 6-axis robot is presented. The aim of the present development is to introduce a new, faster, precise, and more reliable way to calibrate a camera as a robot tool. From the proposed algorithm optimization of the time, it takes to calibrate the camera and start programming is achieved. The time is decreased by 80% and the accuracy and reliability of the system are increased.
An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures

Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the run-time of PF applications becomes increasingly slow. Parallel computing can help to address this. However, resampling (and, hence, PFs as well) necessarily involves a bottleneck, the redistribution step, which is notoriously challenging to parallelize if using textbook parallel computing techniques. A state-of-the-art redistribution takes O((log2N)2) computations on Distributed Memory (DM) architectures, which most supercomputers adopt, whereas redistribution can be performed in O(log2N) on Shared Memory (SM) architectures, such as GPU or mainstream CPUs. In this paper, we propose a novel parallel redistribution for DM that achieves an O(log2N) time complexity. We also present empirical results that indicate that our novel approach outperforms the O((log2N)2) approach.
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other penalties. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods can sometimes show low error on simple-to-reconstruct points that are not part of the training data, for example the all black image. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator (LAD) that does not suffer from these biases. Our anomaly discriminator is trained, similar to the discriminator of a GAN, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data. Further, LAD does not require decoding back to the original data space, which makes anomaly detection possible in domains where it is difficult to define a decoder, such as in irregular graph structured data. Empirically, we show this framework leads to improved performance on image, health record, and graph data.
Regularized twin minimax probability machine for pattern classification and regression

As an excellent discriminant classifier based on generating prior knowledge, the minimax probability machine (MPM) has been widely used and deeply researched in many fields. The core idea of minimax probability machine is to directly estimate probability accuracy bound by minimizing the maximum probability of misclassification. However, minimax probability machine does not include a regularization term for the construction of the separating hyperplane, and it needs to solve a large-scale second-order cone programming problem in the solution process, which greatly limits it development and application. In this paper, to improve the performance of minimax probability machine, we propose a novel binary classification method called regularized twin minimax probability machine classification (TMPMC). The TMPMC constructs two non-parallel hyperplanes for final classification by solving two smaller second-order cone programming problems to improve the performance of the MPM. For each hyperplane, our method is theoretically well grounded on the idea of minimizing the worst case (maximum) probability of misclassification of a class of samples while the distance to the other class is as large as possible. Our approach was first derived as linear methods, and subsequently extended as kernel-based strategies for nonlinear classification. Additionally, we extend TMPMC to the regression problem and propose a new regularized twin minimax probability machine regression (TMPMR). Experimental results on several datasets show that our methods are competitive in terms of generalization performance compared to other algorithms.