Reviewerly/exHarmony huggingface dataset:
DAER: A Resource Preallocation Algorithm of Edge Computing Server by Using Blockchain in Intelligent Driving

The introduction of edge computing (EC) in intelligent driving allows the vehicle to offload tasks to the EC server closer to the vehicle side, creating a new paradigm for task offloading and resource allocation. The movement of the vehicle, the time sensitivity of the processing data, and the resource allocation of the EC server have become bottlenecks of the rapid development of intelligent driving. In this article, we jointly considered the problems of the network economy and resource allocation. In order to eliminate dependence on third parties, we propose a resource transaction architecture based on the blockchain. Moreover, we propose the dynamic allocation algorithm of edge resources (DAERs) based on the double auction mechanism to maximize the satisfaction of users and service providers of edge computing (SPs), where the DAER algorithm is implemented in the form of smart contracts in the blockchain architecture. In particular, we propose the state search algorithm that can improve the prediction accuracy of the staged destination of the vehicle to help allocate resources reasonably. Through simulation experiments, we verify the superior performance of the DAER algorithm in terms of resource utilization rate and the satisfaction of both parties participating in the auction.
Multi-Resolution Multi-Head Attention in Deep Speaker Embedding

Pooling is an essential component to capture long-term speaker characteristics for speaker recognition. This paper proposes simple but effective pooling methods to compute attentive weights for better temporal aggregation over the variable-length input speech, enabling the end-to-end neural network to have improved performance for discriminating among speakers. Particularly, we observe that using multiple heads for attentive pooling over the entire encoded sequence, a method we term as global multi-head attention, significantly improves performance in comparison to various pooling methods, including the recently proposed multi-head attention [1]. To improve diversity of attention heads, we further propose multi-resolution multi-head attention for pooling that has an additional temperature hyperparameter for each head. This leads to even larger performance gain, on top of that achieved using multiple heads. On the benchmark VoxCeleb1 dataset, the proposed method achieves the state-of-the-art performance of Equal Error Rate (EER) of 3.966%. Our analysis shows that using multiple heads and having multiple resolutions on these heads with different temperatures lead to improved certainty of attentive weights in the new state-of-the-art system.
Deep feature importance awareness based no-reference image quality prediction

Deep-learning based image quality assessment (IQA) algorithms usually use the transfer learning method that transfers a pre-trained network for classification task to handle IQA task. Although it can overcome the problem of having insufficient IQA databases to some extent, it cannot distinguish between the important and unimportant deep features for the IQA task, which potentially leads to inaccurate prediction performance. In this paper, we propose a no-reference IQA method based on modelling of deep feature importance. A SE-VGG network is developed by using adaptive transfer learning method. It can suppress the features of local parts of salient objects of images that are not important to the IQA task, and emphasize the features of image distortion and salient objects that are important to IQA task. Moreover, the structure of the SE-VGG is investigated to improve the accuracy of the image quality assessment on a small IQA database. Experiments are conducted to evaluate the performance of the proposed method on various databases, including the LIVE, TID2013, CSIQ, LIVE multiply distorted and LIVE challenge. The results show the proposed method significantly outperforms the state-of-the-art methods. In addition, our method demonstrates a strong generalization ability.
A Framework for Argument Retrieval

Computational argumentation has recently become a fast growing field of research. An argument consists of a claim, such as "We should abandon fossil fuels", which is supported or attacked by at least one premise, for example "Burning fossil fuels is one cause for global warming". From an information retrieval perspective, an interesting task within this setting is finding the best supporting and attacking premises for a given query claim from a large corpus of arguments. Since the same logical premise can be formulated differently, the system needs to avoid retrieving duplicate results and thus needs to use some form of clustering. In this paper we propose a principled probabilistic ranking framework for premises based on the idea of tf-idf that, given a query claim, first identifies highly similar claims in the corpus, and then clusters and ranks their premises, taking clusters of claims as well as the stances of query and premises into account. We compare our approach to a baseline system that uses BM25F which we outperform even with a primitive implementation of our framework utilising BERT.
Separable Robust Reversible Watermarking in Encrypted 2D Vector Graphics

To accomplish robust watermark extraction in reversible watermarking both in plaintext domain and encrypted domain, a separable robust reversible watermarking in encrypted 2D vector graphics is proposed in this paper. Firstly, a content owner uses a key to scramble the polar angles of the vertices to encrypt the graphics in the polar coordinate system. Consequently, a watermark embedder maps the encoded watermark segments to different vertices under the control of an embedding key and an HMAC (hash-based message authentication code) function. After that, the polar angle of the vertex is slightly adjusted to embed a watermark. Since the decryption operation does not affect the embedded watermark, the watermark can be extracted both in the plaintext and encrypted domain. Experimental results and analysis show that the proposed scheme can achieve good invisibility and reversibility. It can effectively resist normal operations such as rotation, scaling, translation (RST) and entity reordering, and it has good robustness against malicious attacks such as vertices/entities addition, deletion and modification.
Vehicle and Vessel Detection on Satellite Imagery: A Comparative Study on Single-Shot Detectors

In this paper, we investigate the feasibility of automatic small object detection, such as vehicles and vessels, in satellite imagery with a spatial resolution between 0.3 and 0.5 m. The main challenges of this task are the small objects, as well as the spread in object sizes, with objects ranging from 5 to a few hundred pixels in length. We first annotated 1500 km2, making sure to have equal amounts of land and water data. On top of this dataset we trained and evaluated four different single-shot object detection networks: YOLOV2, YOLOV3, D-YOLO and YOLT, adjusting the many hyperparameters to achieve maximal accuracy. We performed various experiments to better understand the performance and differences between the models. The best performing model, D-YOLO, reached an average precision of 60% for vehicles and 66% for vessels and can process an image of around 1 Gpx in 14 s. We conclude that these models, if properly tuned, can thus indeed be used to help speed up the workflows of satellite data analysts and to create even bigger datasets, making it possible to train even better models in the future.
Language-Agnostic Multilingual Modeling

Multilingual Automated Speech Recognition (ASR) systems allow for the joint training of data-rich and data-scarce languages in a single model. This enables data and parameter sharing across languages, which is especially beneficial for the data-scarce languages. However, most state-of-the-art multilingual models require the encoding of language information and therefore are not as flexible or scalable when expanding to newer languages. Language-independent multilingual models help to address this issue, and are also better suited for multicultural societies where several languages are frequently used together (but often rendered with different writing systems). In this paper, we propose a new approach to building a language-agnostic multilingual ASR system which transforms all languages to one writing system through a many-to-one transliteration transducer. Thus, similar sounding acoustics are mapped to a single, canonical target sequence of graphemes, effectively separating the modeling and rendering problems. We show with four Indic languages, namely, Hindi, Bengali, Tamil and Kannada, that the language-agnostic multilingual model achieves up to 10% relative reduction in Word Error Rate (WER) over a language-dependent multilingual model.
Multirobot Routing Algorithms for Robots Operating in Vineyards

We consider the problem of multirobot routing in vineyards, a task motivated by our ongoing project aiming at creating a corobotic system to implement precision irrigation on large-scale commercial vineyards. The problem is related to a combinatorial optimization problem on graphs called “team orienteering.” Team orienteering is known to be NP-hard, thus motivating the development of heuristic solutions that can scale to large problem instances. We propose three different parameter-free approaches informed by the domain we consider and compare them against a general purpose heuristic developed previously. In numerous benchmarks derived from data gathered in a commercial vineyard, we demonstrate that our solutions outperform the general purpose heuristic and are scalable, thus allowing us to solve instances with tens of thousands of vertices in the graphs. Note to Practitioners-Routing problems with budget and motion constraints are pervasive to many applications. In particular, the structural constraints considered in this problem are found not only in agricultural environments but also in warehouse logistics and other domains where goods are arranged along regular linear structures. This article proposes and analyzes algorithms that can be applied when multiple agents must be coordinated in these environments. In particular, by utilizing domain-specific knowledge, the solutions proposed in this article outperform general purpose approaches that poorly scale with the size of the environment. The algorithms we present also ensure that no collisions occur between robots-an aspect normally neglected in algorithms previously proposed to solve the team orienteering problem.
An Accurate Facial Expression Detector using Multi-Landmarks Selection and Local Transform Features

In the past few years, facial features detection and landmarks analysis plays a vital role in several practical application such as surveillance system, crime detector and age estimation. In this paper, we proposed a novel approach of recognizing facial expressions based on multi landmark detectors, local transform features and recognizer classifier. The proposed system is divided into four stages. (a) Face detection using skin color segmentation and ellipse fitting, (b) Plotting landmarks on facial features, (c) Feature extraction using euclidean distance, HOG and LBP. While, (d) SVM classification learner is used to classify six basic facial expressions like Neutral, Happy, Sad, Anger, Disgust, and Surprise. The proposed method is applied on two facial expression datasets i-e. MMI facial expressions dataset and Chicago Face dataset and achieved accuracy rates of 80.8% and 83.01%, respectively. The proposed system outperforms the state-of-the-art facial expression recognition system in terms of recognition accuracy. The proposed system should be applicable to different consumer application domains such as online business negotiations, consumer behavior analysis, E-learning environments, and virtual reality practices.
A 2.25 TOPS/W Fully-Integrated Deep CNN Learning Processor with On-Chip Training

This paper presents a deep learning processor that supports both inference and training for the entire convolutional neural network (CNN) with any size. The proposed design enables on-chip training for applications that ask for high security and privacy. Techniques across design abstraction are applied to improve the energy efficiency. Re-arrangement of the weights in filters is leveraged to reduce the processing latency by 88%. Integration of fixed-point and floating-point arithmetics reduces the area of the multiplier by 56.8%, resulting in an unified processing element (PE) with 33% less area. In the low-precision mode, clock gating and data gating are employed to reduce the power of the PE cluster by 62%. Maxpooling and ReLU modules are co-designed to reduce the memory usage by 75%. A modified softmax function is utilized to reduce the area by 78%. Fabricated in 40nm CMOS, the chip consumes 18.7 mW and 64.5 mW for inference and training, respectively, at 82 MHz from a 0.6V supply. It achieves an energy efficiency of 2.25 TOPS/W, which is 2.67 times higher than the state-of-the-art learning processors. The chip also achieves a 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> times higher energy efficiency in training than a high-end CPU.
Defeating NewHope with a Single Trace

The key encapsulation method “NewHope” allows two parties to agree on a secret key. The scheme includes a private and a public key. While the public key is used to encipher a random shared secret, the private key enables to decipher the ciphertext. NewHope is a candidate in the NIST post-quantum project, whose aim is to standardize cryptographic systems that are secure against attacks originating from both quantum and classical computers. While NewHope relies on the theory of quantum-resistant lattice problems, practical implementations have shown vulnerabilities against side-channel attacks targeting the extraction of the private key. In this paper, we demonstrate a new attack on the shared secret. The target consists of the C reference implementation as submitted to the NIST contest, being executed on a Cortex-M4 processor. Based on power measurement, the complete shared secret can be extracted from data of one single trace only. Further, we analyze the impact of different compiler directives. When the code is compiled with optimization turned off, the shared secret can be read from an oscilloscope display directly with the naked eye. When optimizations are enabled, the attack requires some more sophisticated techniques, but the attack still works on single power traces.
A gridded optimization model for photovoltaic applications

This study aims to develop a gridded optimization model for studying photovoltaic applications in Nordic countries. The model uses the spatial and temporal data generated by the mesoscale models STRÅNG and MESAN developed by the Swedish Meteorological and Hydrological Institute. The model is developed based on the comparison between five irradiance databases, three decomposition models, two transposition models, and two photovoltaic models. Several techno-economic and environmental aspects of photovoltaic systems and photovoltaic systems integrated with batteries are investigated from a spatial perspective. CM SAF SARAH-2, Engerer2, and Perez1990 have shown the best performances among the irradiance databases, and decomposition and transposition models, respectively. STRÅNG resulted in the second-best irradiance database to be used in Sweden for photovoltaic applications when comparing hourly global horizontal irradiance with weather station data. The developed model can be employed for carrying out further detailed gridded techno-economic assessments of photovoltaic applications and energy systems in general in Nordic countries. The model structure is generic and can be applied to every gridded climatological database worldwide.
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In this paper, we propose a model for image restoration with blur and impulse noise, it is composed of l1 data fitting term and a nonconvex regularization term, which is the weighted difference of l1-norm and l2-norm based on wavelet frame. The combined model is difficult to solve by the corresponding Euler Lagrangian equations, here we solve it by alternating direction method of multipliers (ADMM). We describe the detailed process of the algorithm, and establish the convergence of the algorithm. The experimental outcomes on different blurs and different impulse noises demonstrate that the proposed approach is better than those existing methods in terms of standard signal-to-noise ratio (PSNR), relative error (ReErr), and visual quality.
Variable Bitrate Image Compression with Quality Scaling Factors

Recently, learned image compression has emerged with significant coding efficiency improvement, and even shown noticeable gains over the state-of-the-art traditional codecs. In the mean time, most existing methods need to train separate models for different bitrate target. In this paper, we propose to embed a set of quality scaling factors (SFs) into learned image compression network, by which we can encode images across an entire bitrate range with a single model. This solution offers the comparable performance with those default approaches requiring multiple bitrate dependent models, and reduces the complexity significantly for practical implementation. Our work also demonstrates the generalization for various compression network structures, image contents, and training loss functions.
Requirements engineering issues causing software development outsourcing failure

Software development outsourcing is becoming more and more famous because of the advantages like cost abatement, process enhancement, and coping with the scarcity of needed resources. Studies confirm that unfortunately a large proportion of the software development outsourcing projects fails to realize anticipated benefits. Investigations into the failures of such projects divulge that in several cases software development outsourcing projects are failed because of the issues that are associated with requirements engineering process. The objective of this study is the identification and the ranking of the commonly occurring issues of the requirements engineering process in the case of software development outsourcing. For this purpose, contemporary literature has been assessed rigorously, issues faced by practitioners have been identified and three questionnaire surveys have been organized by involving experienced software development outsourcing practitioners. The Delphi technique, cut-off value method and 50% rule have also been employed. The study explores 150 issues (129 issues from literature and 21 from industry) of requirements engineering process for software development outsourcing, groups the 150 issues into 7 identified categories and then extricates 43 customarily or commonly arising issues from the 150 issues. Founded on 'frequency of occurrence' the 43 customarily arising issues have been ranked with respect to respective categories (category-wise ranking) and with respect to all the categories (overall ranking). Categories of the customarily arising issues have also been ranked. The issues' identification and ranking contribute to design proactive software project management plan for dealing with software development outsourcing failures and attaining conjectured benefits of the software development outsourcing.
Audio-Attention Discriminative Language Model for ASR Rescoring

End-to-end approaches for automatic speech recognition (ASR) benefit from directly modeling the probability of the word sequence given the input audio stream in a single neural network. However, compared to conventional ASR systems, these models typically require more data to achieve comparable results. Well-known model adaptation techniques, to account for domain and style adaptation, are not easily applicable to end-to-end systems. Conventional HMM-based systems, on the other hand, have been optimized for various production environments and use cases. In this work, we propose to combine the benefits of end-to-end approaches with a conventional system using an attention-based discriminative language model that learns to rescore the output of a first-pass ASR system. We show that learning to rescore a list of potential ASR outputs is much simpler than learning to generate the hypothesis. The proposed model results in up to 8% improvement in word error rate even when the amount of training data is a fraction of data used for training the first-pass system.
Mining Effective Negative Training Samples for Keyword Spotting

Max-pooling neural network architectures have been proven to be useful for keyword spotting (KWS), but standard training methods suffer from a class-imbalance problem when using all frames from negative utterances. To address the problem, we propose an innovative algorithm, Regional Hard-Example (RHE) mining, to find effective negative training samples, in order to control the ratio of negative vs. positive data. To maintain the diversity of the negative samples, multiple non-contiguous difficult frames per negative training utterance are dynamically selected during training, based on the model statistics at each training epoch. Further, to improve model learning, we introduce a weakly constrained max-pooling method for positive training utterances, which constrains max-pooling over the keyword ending frames only at early stages of training. Finally, data augmentation is combined to bring further improvement. We assess the algorithms by conducting experiments on wake-up word detection tasks with two different neural network architectures. The experiments consistently show that the proposed methods provide significant improvements compared to a strong baseline. At a false alarm rate of once per hour, our methods achieve 45-58% relative reduction in false rejection rates over a strong baseline.
A Security Analysis of the Facebook Ad Library

Actors engaged in election disinformation are using online advertising platforms to spread political messages. In response to this threat, online advertising networks have started making political advertising on their platforms more transparent in order to enable third parties to detect malicious advertisers. We present a set of methodologies and perform a security analysis of Facebook's U.S. Ad Library, which is their political advertising transparency product. Unfortunately, we find that there are several weaknesses that enable a malicious advertiser to avoid accurate disclosure of their political ads. We also propose a clustering-based method to detect advertisers engaged in undeclared coordinated activity. Our clustering method identified 16 clusters of likely inauthentic communities that spent a total of over four million dollars on political advertising. This supports the idea that transparency could be a promising tool for combating disinformation. Finally, based on our findings, we make recommendations for improving the security of advertising transparency on Facebook and other platforms.