Reviewerly/exHarmony huggingface dataset:
The who, what, how of software engineering research: a socio-technical framework

Software engineering is a socio-technical endeavor, and while many of our contributions focus on technical aspects, human stakeholders such as software developers are directly affected by and can benefit from our research and tool innovations. In this paper, we question how much of our research addresses human and social issues, and explore how much we study human and social aspects in our research designs. To answer these questions, we developed a socio-technical research framework to capture the main beneficiary of a research study (the who), the main type of research contribution produced (the what), and the research strategies used in the study (how we methodologically approach delivering relevant results given the who and what of our studies). We used this Who-What-How framework to analyze 151 papers from two well-cited publishing venues---the main technical track at the International Conference on Software Engineering, and the Empirical Software Engineering Journal by Springer---to assess how much this published research explicitly considers human aspects. We find that although a majority of these papers claim the contained research should benefit human stakeholders, most focus on technical contributions without engaging humans in their studies. Although our analysis is scoped to two venues, our results suggest a need for more diversification and triangulation of research strategies. In particular, there is a need for strategies that aim at a deeper understanding of human and social aspects of software development practice to balance the design and evaluation of technical innovations. We recommend that the framework should be used in the design of future studies in order to nudge software engineering research towards explicitly including human and social concerns in their designs, and to improve the relevance of our research for human stakeholders.
TrajectoryCNN: A New Spatio-Temporal Feature Learning Network for Human Motion Prediction

Human motion prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new end-to-end feedforward network, TrajectoryCNN, to predict future poses. Compared with the most existing methods, we introduce a new trajectory space and focus on modeling motion dynamics of the input sequence with coupled spatio-temporal features, dynamic local-global features, and global temporal co-occurrence features in the new space. Specifically, the coupled spatio-temporal features describe the spatial and temporal structural information hidden in a natural human motion sequence, which can be easily mined using CNN by simultaneously covering the spatial and temporal dimensions of the sequence with the convolutional filters. The dynamic local-global features encode different correlations among joint trajectories of human motion (i.e. strong correlations among joint trajectories of one part and weak correlations among joint trajectories of different parts), which can be captured by stacking multiple residual trajectory blocks and incorporating our skeletal representation. The global temporal co-occurrence features represent different importance of different input poses to mine the motion dynamics for predicting future poses, which can be obtained automatically by learning free parameters for each pose with our TrajectoryCNN. Finally, we predict future poses with the captured motion dynamic features in a non-recursive manner. Extensive experiments show that our method achieves state-of-the-art performance on five benchmarks (e.g. Human3.6M, CMU-Mocap, 3DPW, G3D, and FNTU), which demonstrates the effectiveness of our proposed method. The code is available at https://github.com/lily2lab/TrajectoryCNN.git.
Subgraph Networks With Application to Structural Feature Space Expansion

Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have been many attempts at learning a representation well-suited for novelty detection and designing a score based on such representation. In this paper, we propose a simple, yet effective method named contrasting shifted instances (CSI), inspired by the recent success on contrastive learning of visual representations. Specifically, in addition to contrasting a given sample with other instances as in conventional contrastive learning methods, our training scheme contrasts the sample with distributionally-shifted augmentations of itself. Based on this, we propose a new detection score that is specific to the proposed training scheme. Our experiments demonstrate the superiority of our method under various novelty detection scenarios, including unlabeled one-class, unlabeled multi-class and labeled multi-class settings, with various image benchmark datasets. Code and pre-trained models are available at this https URL.
Financial development and economic growth nexus in SSA economies: The moderating role of telecommunication development

The economic growth of most sub-Saharan African countries in the past years has not been able to equalize with other regions. Even though financial development has been highlighted in several empirical literature as a factor that could spur up economic growth, the level of financial development in sub-Saharan Africa is not effectively channeled into desired levels of economic growth. However, there is an indication in the literature that financial development will be more relevant to the economic growth of sub-Saharan African economies that have strong telecommunication infrastructure. Using the system General Method of Moment estimation technique, the paper found that telecommunication infrastructure enhances the effect of financial development on the economic growth of sub-Saharan African economies. It is therefore recommended that sub-Saharan African economies should apply appropriate measures to boost their telecommunication infrastructure so that gains from the financial sector can effectively be channeled into economic growth.
HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking

Tracking moving objects from space-borne satellite videos is a new and challenging task. The main difficulty stems from the extremely small size of the target of interest. First, because the target usually occupies only a few pixels, it is hard to obtain discriminative appearance features. Second, the small object can easily suffer from occlusion and illumination variation, making the features of objects less distinguishable from features in surrounding regions. Current state-of-the-art tracking approaches mainly consider high-level deep features of a single frame with low spatial resolution, and hardly benefit from inter-frame motion information inherent in videos. Thus, they fail to accurately locate such small objects and handle challenging scenarios in satellite videos. In this article, we successfully design a lightweight parallel network with a high spatial resolution to locate the small objects in satellite videos. This architecture guarantees real-time and precise localization when applied to the Siamese Trackers. Moreover, a pixel-level refining model based on online moving object detection and adaptive fusion is proposed to enhance the tracking robustness in satellite videos. It models the video sequence in time to detect the moving targets in pixels and has ability to take full advantage of tracking and detecting. We conduct quantitative experiments on real satellite video datasets, and the results show the proposed HIGH-RESOLUTION SIAMESE NETWORK (HRSiam) achieves state-of-the-art tracking performance while running at over 30 FPS.
Ultrabroadband Entangled Photons on a Nanophotonic Chip

The development of quantum technologies on nanophotonic platforms has seen momentous progress in the past decade. Despite that, a demonstration of time-frequency entanglement over a broad spectral width is still lacking. Here we present an efficient source of ultrabroadband entangled photon pairs on a periodically poled lithium niobate nanophotonic waveguide. Employing dispersion engineering, we demonstrate a record-high 100 THz (1.2 μm-2 μm) generation bandwidth with a high efficiency of 13 GHz/mW and excellent noise performance with the coincidence-to-accidental ratio exceeding 10^{5}. We also measure strong time-frequency entanglement with over 98% two-photon interference visibility.
CAPNet: Continuous Approximation Projection for 3D Point Cloud Reconstruction Using 2D Supervision

Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for datadriven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.
DL-RRT* algorithm for least dose path Re-planning in dynamic radioactive environments

One of the most challenging safety precautions for workers in dynamic, radioactive environments is avoiding radiation sources and sustaining low exposure. This paper presents a sampling-based algorithm, DL-RRT*, for minimum dose walk-path re-planning in radioactive environments, expedient for occupational workers in nuclear facilities to avoid unnecessary radiation exposure. The method combines the principle of random tree star (RRT*) and D* Lite, and uses the expansion strength of grid search strategy from D* Lite to quickly find a high-quality initial path to accelerate convergence rate in RRT*. The algorithm inherits probabilistic completeness and asymptotic optimality from RRT* to refine the existing paths continually by sampling the search-graph obtained from the grid search process. It can not only be applied to continuous cost spaces, but also make full use of the last planning information to avoid global re-planning, so as to improve the efficiency of path planning in frequently changing environments. The effectiveness and superiority of the proposed method was verified by simulating radiation field under varying obstacles and radioactive environments, and the results were compared with RRT* algorithm output.
Lorentz Boost Networks: autonomous physics-inspired feature engineering

We present a two-stage neural network architecture that enables a fully autonomous and comprehensive characterization of collision events by exclusively exploiting the four-momenta of final-state particles. We refer to the first stage of the architecture as Lorentz Boost Network (LBN). The LBN allows the creation of particle combinations representing rest frames. The LBN also enables the formation of further composite particles, which are then transformed into said rest frames by Lorentz transformation. The properties of the composite, transformed particles are compiled in the form of characteristic variables that serve as input for a subsequent network. This second network has to be configured for a specific analysis task such as the separation of signal and background events. Using the example of the classification of ttH and t+b events, we compare the separation power of the LBN approach with that of domain-unspecific deep neural networks (DNN). We observe leading performance with the LBN, even though we provide the DNNs with extensive additional input information beyond the particle four-momenta. Furthermore, we demonstrate that the LBN forms physically meaningful particle combinations and autonomously generates suitable characteristic variables.
What Can Neural Networks Reason About

Neural networks have succeeded in many reasoning tasks. Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many such tasks, while less structured networks fail. Theoretically, there is limited understanding of why and when a network structure generalizes better than other equally expressive ones. We develop a framework to characterize which reasoning tasks a network can learn well, by studying how well its structure aligns with the algorithmic structure of the relevant reasoning procedure. We formally define algorithmic alignment and derive a sample complexity bound that decreases with better alignment. This framework explains the empirical success of popular reasoning models and suggests their limitations. We unify seemingly different reasoning tasks, such as intuitive physics, visual question answering, and shortest paths, via the lens of a powerful algorithmic paradigm, dynamic programming (DP). We show that GNNs can learn DP and thus solve these tasks. On several reasoning tasks, our theory aligns with empirical results.
Cryptographic Group Actions and Applications

Isogeny-based assumptions have emerged as a viable option for quantum-secure cryptography. Recent works have shown how to build efficient (public-key) primitives from isogeny-based assumptions such as CSIDH and CSI-FiSh. However, in its present form, the landscape of isogenies does not seem very amenable to realizing new cryptographic applications. Isogeny-based assumptions often have unique efficiency and security properties, which makes building new cryptographic applications from them a potentially tedious and time-consuming task. In this work, we propose a new framework based on group actions that enables the easy usage of a variety of isogeny-based assumptions. Our framework generalizes the works of Brassard and Yung (Crypto’90) and Couveignes (Eprint’06). We provide new definitions for group actions endowed with natural hardness assumptions that model isogeny-based constructions amenable to group actions such as CSIDH and CSI-FiSh. We demonstrate the utility of our new framework by leveraging it to construct several primitives that were not previously known from isogeny-based assumptions. These include smooth projective hashing, dual-mode PKE, two-message statistically sender-private OT, and Naor-Reingold style PRF. These primitives are useful building blocks for a wide range of cryptographic applications. We introduce a new assumption over group actions called Linear Hidden Shift (LHS) assumption. We then present some discussions on the security of the LHS assumption and we show that it implies symmetric KDM-secure encryption, which in turn enables many other primitives that were not previously known from isogeny-based assumptions.
OCNLI: Original Chinese Natural Language Inference

Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable datasets for most of the world's languages.In this paper, we present the first large-scale NLI dataset (consisting of ∼56,000 annotated sentence pairs) 1 for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI).Unlike recent attempts at extending NLI to other languages, our dataset does not rely on any automatic translation or non-expert annotation.Instead, we elicit annotations from native speakers specializing in linguistics.We follow closely the annotation protocol used for MNLI, but create new strategies for eliciting diverse hypotheses.We establish several baseline results on our dataset using state-of-the-art pre-trained models for Chinese, and find even the best performing models to be far outpaced by human performance (∼12% absolute performance gap), making it a challenging new resource that we hope will help to accelerate progress in Chinese natural language understanding.To the best of our knowledge, this is the first humanelicited MNLI-style corpus for a non-English language.
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation

We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source of the directed graph (e.g. strain history) to the target (e.g. stress) measured by an objective function. Meanwhile, the data agent, which is tasked with generating data from real or virtual experiments (e.g. molecular dynamics, discrete element simulations), interacts with the modeling agent sequentially and uses reinforcement learning to design new experiments to optimize the prediction capacity. Consequently, this treatment enables us to emulate an idealized scientific collaboration as selections of the optimal choices in a decision tree search done automatically via deep reinforcement learning.
Z-Inspection<sup>®</sup>: A Process to Assess Trustworthy AI

The ethical and societal implications of artificial intelligence systems raise concerns. In this article, we outline a novel process based on applied ethics, namely, Z-Inspection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , to assess if an AI system is trustworthy. We use the definition of trustworthy AI given by the high-level European Commission's expert group on AI. Z-Inspection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is a general inspection process that can be applied to a variety of domains where AI systems are used, such as business, healthcare, and public sector, among many others. To the best of our knowledge, Z-Inspection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is the first process to assess trustworthy AI in practice.
Reconfigurable Quantum Local Area Network Over Deployed Fiber

Three distinct campus buildings are connected using time-synchronized signals and flex-grid entanglement distribution, leading to the first deployed fiber network demonstration of remote state preparation.
SS-IL: Separated Softmax for Incremental Learning

We consider class incremental learning (CIL) problem, in which a learning agent continuously learns new classes from incrementally arriving training data batches and aims to predict well on all the classes learned so far. The main challenge of the problem is the catastrophic forgetting, and for the exemplar-memory based CIL methods, it is generally known that the forgetting is commonly caused by the classification score bias that is injected due to the data imbalance between the new classes and the old classes (in the exemplar-memory). While several methods have been proposed to correct such score bias by some additional post-processing, e.g., score re-scaling or balanced fine-tuning, no systematic analysis on the root cause of such bias has been done. To that end, we analyze that computing the softmax probabilities by combining the output scores for all old and new classes could be the main cause of the bias. Then, we propose a new method, dubbed as Separated Softmax for Incremental Learning (SS-IL), that consists of separated softmax (SS) output layer combined with task-wise knowledge distillation (TKD) to resolve such bias. Throughout our extensive experimental results on several large-scale CIL benchmark datasets, we show our SS-IL achieves strong state-of-the-art accuracy through attaining much more balanced prediction scores across old and new classes, without any additional post-processing.
Inferring Streaming Video Quality from Encrypted Traffic

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. We develop models that infer quality metrics (\ie, startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (\eg, due to aggregation). Second, we develop a single composite model that works for a range of different services (\ie, Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (\eg, the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed'' and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.