Reviewerly/exHarmony huggingface dataset:
A Sliced Wasserstein Loss for Neural Texture Synthesis

We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. VGG-19). The underlying mathematical problem is the measure of the distance between two distributions in feature space. The Gram-matrix loss is the ubiquitous approximation for this problem but it is subject to several shortcomings. Our goal is to promote the Sliced Wasserstein Distance as a replacement for it. It is theoretically proven, practical, simple to implement, and achieves results that are visually superior for texture synthesis by optimization or training generative neural networks.
Neural Architecture Search with Random Labels

In this paper, we investigate a new variant of neural architecture search (NAS) paradigm – searching with random labels (RLNAS). The task sounds counter-intuitive for most existing NAS algorithms since random label provides few information on the performance of each candidate architecture. Instead, we propose a novel NAS framework based on ease-of-convergence hypothesis, which requires only random labels during searching. The algorithm involves two steps: first, we train a SuperNet using random labels; second, from the SuperNet we extract the subnetwork whose weights change most significantly during the training. Extensive experiments are evaluated on multiple datasets (e.g. NAS-Bench-201 and ImageNet) and multiple search spaces (e.g. DARTS-like and MobileNet-like). Very surprisingly, RLNAS achieves comparable or even better results compared with state-of-the-art NAS methods such as PC-DARTS, Single Path One-Shot, even though the counterparts utilize full ground truth labels for searching. We hope our finding could inspire new understandings on the essential of NAS.
LAFEAT: Piercing Through Adversarial Defenses with Latent Features

Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> -norm white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent on the effective use of the defender’s hidden components, and it should no longer be viewed from a holistic perspective.
It’s alignment all the way down, but not all the way up: Speakers align on some features but not others within a dialogue

During conversation, speakers modulate characteristics of their production to match their interlocutors’ characteristics. This behavior is known as alignment. Speakers align at many linguistic levels, including the syntactic, lexical, and phonetic levels. As a result, alignment is often treated as a unitary phenomenon, in which evidence of alignment on one feature is cast as alignment of the entire linguistic level. This experiment investigates whether alignment can occur at some levels but not others, and on some features but not others, within a given dialogue. Participants interacted with two experimenters with highly contrasting acoustic-phonetic and syntactic profiles. The experimenters each described sets of pictures using a consistent acoustic-phonetic and syntactic profile; the participants then described new pictures to each experimenter individually. Alignment was measured as the degree to which subjects matched their current listener’s speech (vs. their non-listener’s) on each of several individual acoustic-phonetic and syntactic features. Additionally, a holistic measure of phonetic alignment was assessed using 323 acoustic-phonetic features analyzed jointly in a machine learning classifier. Although participants did not align on several individual spectral-phonetic or syntactic features, they did align on individual temporal-phonetic features and as measured by the holistic acoustic-phonetic profile. Thus, alignment can simultaneously occur at some levels but not others within a given dialogue, and is not a single phenomenon but rather a constellation of loosely-related effects. These findings suggest that the mechanism underlying alignment is not a primitive, automatic priming mechanism but rather guided by communicative or social factors.
A robust and multi chaotic DNA image encryption with pixel-value pseudorandom substitution scheme

A color image DNA encryption system, based on multi optical chaos and pixel-value pseudorandom substitution, is proposed. In the scheme, four twin-slave-laser-pairs (TSLPs) subject to the optical injection from the chaotic master laser (ML) generate the four respectively synchronized chaotic signals. Three of these signals are used to generate keys, which are performed DNA operations with three components of color image, and the rest chaotic signals are utilized to control the choice of styles of DNA operations. The proposed image encryption algorithm includes pixel-value pseudorandom substitution based on global bit scrambling and image diffusion based on DNA encryption. After the group velocity dispersion (GVD) inside 50 km long SMF is compensated by a 1 km long DCF, the data can be well recovered at receiving end. The research results highlight that our cipher system can well achieve the encryption and decryption of color image, and is effective and highly secure to resist a variety of attacks by analyzing the randomness, key sensitivity, statistical feature, information entropy, differential performance, noise effect, PSNR and MSE of image in encryption and decryption process. The scheme is promising for color image encryption application in optical communication.
GrimoireLab: A toolset for software development analytics

After many years of research on software repositories, the knowledge for building mature, reusable tools that perform data retrieval, storage and basic analytics is readily available. However, there is still room to improvement in the area of reusable tools implementing this knowledge.To produce a reusable toolset supporting the most common tasks when retrieving, curating and visualizing data from software repositories, allowing for the easy reproduction of data sets ready for more complex analytics, and sparing the researcher or the analyst of most of the tasks that can be automated.Use our experience in building tools in this domain to identify a collection of scenarios where a reusable toolset would be convenient, and the main components of such a toolset. Then build those components, and refine them incrementally using the feedback from their use in both commercial, community-based, and academic environments.GrimoireLab, an efficient toolset composed of five main components, supporting about 30 different kinds of data sources related to software development. It has been tested in many environments, for performing different kinds of studies, and providing different kinds of services. It features a common API for accessing the retrieved data, facilities for relating items from different data sources, semi-structured storage for easing later analysis and reproduction, and basic facilities for visualization, preliminary analysis and drill-down in the data. It is also modular, making it easy to support new kinds of data sources and analysis.We present a mature toolset, widely tested in the field, that can help to improve the situation in the area of reusable tools for mining software repositories. We show some scenarios where it has already been used. We expect it will help to reduce the effort for doing studies or providing services in this area, leading to advances in reproducibility and comparison of results.
A Novel Video Salient Object Detection Method via Semisupervised Motion Quality Perception

Previous video salient object detection (VSOD) approaches have mainly focused on the perspective of network design for achieving performance improvements. However, with the recent slowdown in the development of deep learning techniques, it might become increasingly difficult to anticipate another breakthrough solely via complex networks. Therefore, this paper proposes a universal learning scheme to obtain a further 3% performance improvement for all state-of-the-art (SOTA) VSOD models. The major highlight of our method is that we propose the ‘motion quality’, a new concept for mining video frames from the ‘buffered’ testing video stream for constructing a fine-tuning set. By using our approach, all frames in this set can all well-detect their salient object by the ‘target SOTA model’ — the one we want to improve. Thus, the VSOD results of the mined set, which were previously derived by the target SOTA model, can be directly applied as pseudolearning objectives to fine-tune a completely new spatial model that has been pretrained on the widely used DAVIS-TR set. Since some spatial scenes in the buffered testing video stream are shown, the fine-tuned spatial model can perform very well for the remaining unseen testing frames, outperforming the target SOTA model significantly. Although offline model fine tuning requires additional time costs, the performance gain can still benefit scenarios without speed requirements. Moreover, its semisupervised methodology might have considerable potential to inspire the VSOD community in the future.
Analysis and Evaluation of Barriers Influencing Blockchain Implementation in Moroccan Sustainable Supply Chain Management: An Integrated IFAHP-DEMATEL Framework

Blockchain technology has received wide attention during recent years, and has huge potential to transform and improve supply chain management. However, its implementation in the SSCM (Sustainable Supply Chain Management) strategy is sophisticated, and the challenges are not explored very well, especially in the Moroccan context. To this end, the chief objective of the current endeavor is to investigate the barriers that hinder the adoption of blockchain technology in SSCM from the Moroccan industry and service sectors’ perspective. Based on a comprehensive literature search and the use of experts’ viewpoints, the barriers affecting the successful implementation of blockchain are classified into three categories called TEO: technological and system, environmental, and intra-organizational dimensions. In this context, a fuzzy group decision-making framework is organized by combining DEMATEL (Decision-Making Trial and Evaluation Laboratory) and IFAHP (Intuitionistic Fuzzy Analytic Hierarchy Process). The IFAHP technique helps to determine the importance/priorities of barriers affecting blockchain adoption, while the DEMATEL technique forms the cause–effect interconnections between these barriers and classifies them concerning the degree of importance and relationships. The results reveal that ‘government policy and support’ and ‘challenges in integrating sustainable practices and blockchain technology through SCM’ are significant adoption barriers of blockchain in Moroccan SSCM. The proposed solution can support industrial decision makers to form flexible short- and long-term decision-making strategies to efficiently manage a sustainable supply chain.
Efficient Verification of Edge Data Integrity in Edge Computing Environment

The new edge computing paradigm extends cloud computing by allowing service vendors to deploy their service instances and data on distributed edge servers to serve their service users in close geographic proximity to those edge servers. Caching edge data on edge servers profoundly reduces the retrieval latency perceived by users. However, these edge data are subject to corruption due to intentional and/or accidental exceptions. This is a major challenge for service vendors but has been overlooked. Thus, verifying the integrity of edge data accurately and efficiently is a critical security problem in the edge computing environment. A unique characteristic of the edge computing environment is that edge servers suffer from constrained computing capacities. Thus, verifying data integrity on massive edge servers individually is computationally expensive and impractical. In this paper, we tackle this Edge Data Integrity (EDI) problem with an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows service vendors to inspect data integrity and localize corrupted edge data cached on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.
Riggable 3D Face Reconstruction via In-Network Optimization

This paper presents a method for riggable 3D face reconstruction from monocular images, which jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations. To achieve this goal, we design an end-to-end trainable network embedded with a differentiable in-network optimization. The network first parameterizes the face rig as a compact latent code with a neural decoder, and then estimates the latent code as well as per-image parameters via a learnable optimization. By estimating a personalized face rig, our method goes beyond static reconstructions and enables downstream applications such as video retargeting. In-network optimization explicitly enforces constraints derived from the first principles, thus introduces additional priors than regression-based methods. Finally, data-driven priors from deep learning are utilized to constrain the ill-posed monocular setting and ease the optimization difficulty. Experiments demonstrate that our method achieves SOTA reconstruction accuracy, reasonable robustness and generalization ability, and supports standard face rig applications.
Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain

This letter presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terrain-vehicle interaction, or assume a set of discrete terrain classes. However, when operating on unstructured terrain, especially at high speeds, even small variations in the environment will be magnified and cause inaccurate plan execution. In this letter, to capture the complex kinodynamic model and mathematically unknown world state, we learn a kinodynamic planner in a data-driven manner with onboard inertial observations. Our approach is tested on a physical robot in different indoor and outdoor environments, enables fast and accurate off-road navigation, and outperforms environment-independent alternatives, demonstrating 52.4% to 86.9% improvement in terms of plan execution success rate while traveling at high speeds.
Two-sided preferences task matching mechanisms for blockchain-based crowdsourcing

In this paper, novel task matching mechanisms with two-sided preferences of workers and requesters are proposed for blockchain-based crowdsourcing. Existing blockchain-based crowdsourcing frameworks match workers to tasks using allocation mechanisms considering metrics such as cost, location, and workers’ reputation to answer requesters’ requirements. However, they still match workers to tasks through mechanisms that are requester-biased with no consideration for workers’ preferences. This may lead to workers rejecting or neglecting their allocated tasks. As a solution, we propose two-sided preferences task matching mechanisms for blockchain-based crowdsourcing, namely SenseChain+, and Gale–Shapley Matching (GSM). In order to do the matching, the proposed mechanisms utilize the preferences of workers based on a proposed Quality-of-Task (QoT) metric, and the preferences of tasks based on a Quality-of-Information (QoI) metric. To ensure their autonomous, reliable, and transparent execution, these matching mechanisms are integrated in an existing blockchain-based crowdsourcing framework, implemented using smart contracts. The mechanisms and framework are implemented using Solidity on a private blockchain and evaluated using a real dataset. They are benchmarked to the Nearest Neighbor Matching (NNM) mechanism. The proposed mechanisms demonstrate higher performance compared to the benchmark in terms of workers’ QoI, payment, satisfaction, and confidence. To demonstrate the need for each mechanism, the performance under different demand to supply contexts is measured in terms of the workers’ QoI, confidence, and the minimum payment. Each proposed matching mechanism was found to outperform the others in a range of demand to supply ratios. Finally, the proposed matching mechanisms are stable and feasible on-chain with reasonable execution cost.
A region-based hypergraph network for joint entity-relation extraction

In joint entity and relation extraction, the input document is divided into multiple potential entity regions and context regions, where the characteristics of entities and their relations can often be reflected in the context. Therefore, an effective joint modeling method designed toward the features of different regions can lead to superior performance of joint entity and relation extraction. Previous works tend to implement in-depth modeling only for potential entity regions, ignoring the importance of contextual information for joint entity and relation extraction. In this paper, we propose a Region-based Hypergraph Network (RHGN) for joint entity and relation extraction. The RHGN introduces the concept of regional hypernodes for the first time, and proposes a cooperative method of GCN and BiLSTM to generate hypernodes for each region. Then, a region-based relation hypergraph is constructed for fairly and efficiently aggregate the features of all regions in the sentence. In order to initialize and update the features of the edges and hypernodes in the hypergraph, a Sequence-Enhanced Graph (SEG) unit is designed. Finally, we perform comparison experiments with existing competitive models on three public datasets: the CoNLL04, SciERC and ADE datasets. Experimental results demonstrate that our model achieves a significant improvement over the previous models on both entity recognition and relation extraction, and it also shows superior performance for dataset with nested entities. Extensive additional experiments further confirm the effectiveness of our approach.
Spatial variations in soil organic carbon, nitrogen, phosphorus contents and controlling factors across the “Three Rivers” regions of southwest China

Based on the data of China Geochemical Baselines project, geostatistical analysis was used to investigate the spatial variations in soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) contents across the “Three Rivers” regions of southwest China, and the factors affecting them were analyzed by the redundancy analysis (RDA) and Person's correlation. Results showed that, the average content of SOC, TN and TP in the study area were 7.20 g/kg, 0.84 g/kg and 0.55 g/kg, respectively. The SOC and TN contents showed an obvious enrichment characteristic with great spatial variability, while TP content was stable on regional scale. The SOC, TN and TP contents decreased with elevation increase in the northern frigid highland, but showed an opposite character in the southern tropical & subtropical, which actually reflected the control of temperature on them. Combined with that there were higher SOC, TN and TP contents in subalpine meadow soil and red earth–yellow earth of the middle latitude zone, these suggested that the suitable temperature was conducive to the accumulation of soil nutrients. The weak positive correlation between population density and soil nutrients, together with high level of soil nutrients in the vicinity of large cities, demonstrated that human activities had significantly increased the soil nutrients contents in the study area. The RDA results showed that soil nutrients in the northern frigid highland were mainly controlled by the environmental factors dominated by temperature and soil structural factors dominated by parent materials with the total explanatory power as high as 75.87%, while in the southern tropical & subtropical mainly by the environmental factors dominated by chemical and biological weathering and the biological factors dominated by vegetation with the total explanatory power as high as 88.53%. The above factors superimposing a certain degree of human activities converged to cause that the SOC and TP contents in the south were higher than that in the north while the TN content was lower than that in the north.
Fixing dependency errors for Python build reproducibility

Software reproducibility is important for re-usability and the cumulative progress of research. An important manifestation of unreproducible software is the changed outcome of software builds over time. While enhancing code reuse, the use of open-source dependency packages hosted on centralized repositories such as PyPI can have adverse effects on build reproducibility. Frequent updates to these packages often cause their latest versions to have breaking changes for applications using them. Large Python applications risk their historical builds becoming unreproducible due to the widespread usage of Python dependencies, and the lack of uniform practices for dependency version specification. Manually fixing dependency errors requires expensive developer time and effort, while automated approaches face challenges of parsing unstructured build logs, finding transitive dependencies, and exploring an exponential search space of dependency versions. In this paper, we investigate how open-source Python projects specify dependency versions, and how their reproducibility is impacted by dependency packages. We propose a tool PyDFix to detect and fix unreproducibility in Python builds caused by dependency errors. PyDFix is evaluated on two bug datasets BugSwarm and BugsInPy, both of which are built from real-world open-source projects. PyDFix analyzes a total of 2,702 builds, identifying 1,921 (71.1%) of them to be unreproducible due to dependency errors. From these, PyDFix provides a complete fix for 859 (44.7%) builds, and partial fixes for an additional 632 (32.9%) builds.