Reviewerly/exHarmony huggingface dataset:
LC: A Flexible, Extensible Open-Source Toolkit for Model Compression

The continued increase in memory, runtime and energy consumption of deployed machine learning models on one side, and the trend to miniaturize intelligent devices and sensors on the other side, imply that model compression will remain a critical need for the foreseeable future. A scalable solution to this problem must be able to handle arbitrary choices of the reference model to be compressed (driven by the machine learning task), of the form of compression to use, and of the costs and constraints to obey (driven by the target device). We describe an open-source toolkit that is primarily designed to be flexible and extensible, but which is also efficient in compression time and achieves state-of-the-art accuracy-compression curves, as demonstrated empirically over a number of deep net architectures. Mathematically, this is achieved by formulating compression as a constrained optimization using auxiliary variables that facilitate separability, and solving it via a penalty method and alternating optimization, which results in a "learning-compression" (LC) algorithm. This alternates a "learning" step over the original model, independent of the compression, and a "compression" step over the compressed parameters, independent of the dataset and task. Each step can typically be solved by reusing well-known algorithms, such as SGD or EM in the learning step, or SVD or k-means in the compression step, and this makes the algorithm flexible and extensible. The toolkit is available at https://github.com/UCMerced-ML/LC-model-compression.
Security analysis of uppercaseSM9 digital signature and key encapsulation

Security analysis provides strong guarantees and evidence for security cryptosystems. SM9 is an identity-based cryptosystem designed by China and has become a Chinese standard. The SM9 digital signature and encryption algorithm also became ISO/IEC International standards. However, there are few published research results on the security of SM9 cryptosystems. Based on Gap-$q$-BCAA1 assumption, Cheng gave the security analysis of SM9 key exchange protocol, key encapsulation and encryption algorithm in Inscrypt 2018. In this paper, we first give the formal security analysis for SM9 digital signature. Based on the $q$-SDH assumption, we prove that SM9 signature algorithm is EUF-CMIA secure. To eliminate the Gap assumption, we then use the technique of Twin-Hash-ElGamal to modify SM9 key encapsulation slightly without compromising its security and propose a new identity-based key encapsulation mechanism called Twin-SM9. Compared to SM9 key encapsulation, both the system public key and user private key contain one additional group element only and the ciphertext size remains the same. We prove that Twin-SM9 achieves IND-CCA security in the random oracle model based on the $q$-BDHI assumption. Our results clarify the security of SM9 and are useful for the design of SM9-based cryptosystems.
Inferring regional access network topologies

Using a toolbox of Internet cartography methods, and new ways of applying them, we have undertaken a comprehensive active measurement-driven study of the topology of U.S. regional access ISPs. We used state-of-the-art approaches in various combinations to accommodate the geographic scope, scale, and architectural richness of U.S. regional access ISPs. In addition to vantage points from research platforms, we used public WiFi hotspots and public transit of mobile devices to acquire the visibility needed to thoroughly map access networks across regions. We observed many different approaches to aggregation and redundancy, across links, nodes, buildings, and at different levels of the hierarchy. One result is substantial disparity in latency from some Edge COs to their backbone COs, with implications for end users of cloud services. Our methods and results can inform future analysis of critical infrastructure, including resilience to disasters, persistence of the digital divide, and challenges for the future of 5G and edge computing.
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization

The copying mechanism has had considerable success in abstractive summarization, facilitating models to directly copy words from the input text to the output summary.Existing works mostly employ encoder-decoder attention, which applies copying at each time step independently of the former ones.However, this may sometimes lead to incomplete copying.In this paper, we propose a novel copying scheme named Correlational Copying Network (CoCoNet) that enhances the standard copying mechanism by keeping track of the copying history.It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one.In addition, we strengthen CoCoNet through pretraining with suitable corpora that simulate the copying behaviors.Experimental results show that CoCoNet can copy more accurately and achieves new state-of-the-art performances on summarization benchmarks, including CNN/DailyMail for news summarization and SAMSum for dialogue summarization.Our code is available at https:// github.com/hrlinlp/coconet.
Oblivious Linear Group Actions and Applications

In this paper we propose efficient two-party protocols for obliviously applying a (possibly random) linear group action to a data set. Our protocols capture various applications such as oblivious shuffles, circular shifts, matrix multiplications, to name just a few. A notable feature enjoyed by our protocols, is that they admit a round-optimal (more precisely, one-round) online computation phase, once an input-independent off-line computation phase has been completed. Our oblivious shuffle is the first to achieve a round-optimal online phase. The most efficient instantiations of our protocols are obtained in the so-called client-aided client-server setting, where the offline phase is run by a semi-honest input party (client) who will then distribute the generated correlated randomness to the computing parties (servers). When comparing the total running time to the previous best two-party oblivious shuffle protocol by Chase et al. (Asiacrypt 2020), our shuffle protocol in this client-aided setting is up to 105 times and 152 times faster, in the LAN and WAN setting, respectively. We additionally show how the Chase et al. protocol (which is a standard two-party protocol) can be modified to leverage the advantages of the client-aided setting, but show that, even doing so, our scheme is still two times faster in the online phase and 1.34 times faster in total on average.
Application of Sigma Point Particle Filter Method for Passive State Estimation in Underwater

Bearings-only tracking (BOT) plays a vital role in underwater surveillance. In BOT, measurement is tangentially related to state of the system. This measurement is also corrupted with noise due to turbulent underwater environment. Hence state estimation process using BOT becomes nonlinear. This necessitates the use of nonlinear filtering algorithms in place of traditional linear filters like Kalman filter. In general, these nonlinear filters utilize the assumption of measurements being corrupted with Gaussian noise for state estimation. The measurements cannot be always corrupted with Gaussian noise because of the highly unstable sea environment. These problems indicate the necessity for development of nonlinear non-Gaussian filters like particle filter (PF) for underwater tracking. However, PF suffers from severe problems like sample degeneracy and impoverishment and also it is tedious to select an appropriate technique for resampling. To overcome these difficulties in PF implementation, the strategy of combining PF with another filter like unscented Kalman filter is proposed for target’s state estimation. The detailed analysis of the same is presented in comparison with other particle filter combinations using the simulation results obtained in Matlab.
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention

Multi-label few-shot image classification (ML-FSIC) is the task of assigning descriptive labels to previously unseen images, based on a small number of training examples. A key feature of the multi-label setting is that images often have multiple labels, which typically refer to different regions of the image. When estimating prototypes, in a metric-based setting, it is thus important to determine which regions are relevant for which labels, but the limited amount of training data makes this highly challenging. As a solution, in this paper we propose to use word embeddings as a form of prior knowledge about the meaning of the labels. In particular, visual prototypes are obtained by aggregating the local feature maps of the support images, using an attention mechanism that relies on the label embeddings. As an important advantage, our model can infer prototypes for unseen labels without the need for fine-tuning any model parameters, which demonstrates its strong generalization abilities. Experiments on COCO and PASCAL VOC furthermore show that our model substantially improves the current state-of-the-art.
Solar Transposition Modeling via Deep Neural Networks With Sky Images

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -dimensional vector, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. These profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.
A Preliminary Study on Quality of Experience Assessment of Compressed Audio File Format

Audio compression formats reduce the file size before downloading and uploading on the Cloud and social media platforms. An audio codec performs this operation. The uncompressed or lossless format can become lossless and lossy compressed files shrinking file size due to compression. Uncompressed file format change not only decreases the original size but also cuts the audio quality. The bit rate also drops. One assesses the quality of experience (QoE) of compressed audio file formats by comparing them to the original audios. One needs to conduct subjective QoE assessment experiments of both formats. This can be done by uploading a compressed file on the Cloud depending on its quality and sample rate to determine the satisfaction level of the end-user and which audio quality can be uploaded on the cloud, including other social media platforms.
Smart contract-based secure cooperative spectrum sensing algorithm

Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users’ transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU’s utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack.
An Interactive Information System That Supports an Augmented Reality Game in the Context of Game-Based Learning

Mobile augmented reality applications are gaining prominence in education, but there is a need to design appropriate and enjoyable games to be used in educational contexts such as classrooms. This paper presents an interactive information system designed to support the implementation of an augmented reality application in the context of game-based learning. PlanetarySystemGO includes a location-based mobile augmented reality game designed to promote learning about the celestial bodies and planetary systems of the Universe, and a web application that interacts with the mobile device application. Besides face-to-face classes, this resource can also be used in online classes, which is very useful in social isolation situations as the ones caused by the COVID-19 pandemic. Furthermore, it is the inclusion of the web application, with a back-office, in the information system that makes it possible to include curricula contents according to the grade level of students. Moreover, it is intended that teachers use the information system to include the contents they find appropriate to the grade level they teach. Therefore, it is crucial to provide their professional development to be able to use this resource. In this regard, a pilot study was conducted with teachers who participated in a STEM professional development programme in order to assess if the system is appropriate to be used by them. It is concluded that teachers found this resource relevant to motivate students to learn, and also acknowledged that the web application facilitated the introduction of appropriate curricula contents and also was useful to assess student performance during the game. Teachers need support, however, to implement these types of technologies which are not familiar to them. The necessary support can be provided through collaboration among the researchers and teachers in their schools. Besides engaging students to learn about celestial bodies, it is concluded that the information system can be used by teachers to introduce appropriate curricula contents and to be implemented in class.
Modelling and forecasting for solar irradiance from solarimetric station

This paper proposes two approaches for modeling solar irradiance time series. The first is an exploratory analysis and second is a periodic forecast model for solar irradiance data from solarimetric station in a country city of Bahia, Brazil. Two normality hypothesis tests were applied, Anderson-Darling and Shapiro-Wilk. The shape and symmetry of the data were also analyzed using boxplot and histograms to investigate the extreme points, as well as great asymmetry in the distribution of data at different times. Through adherence tests, the normal distribution of solar irradiance in most hours within the annual periodic is rejected. A study by season was carried out, which showed a different behavior in relation to the symmetry of the data, with autumn being the most uniform and spring being the most stochastic. Spring and summer presented better conditions for installation of photovoltaic plates, due to the high solar irradiance rates, and autumn and winter presented satisfactory solar irradiance to maintain this form of generation throughout the year.The second approach is a forecast to solar irradiance in photovoltaic generation systems. The importance of this forecast type is to favor the routine of planning, operation and maintenance of these types of systems, in addition to serving as a basis for feasibility studies and expansion of solar generation. Given a stochastic resource and the periodic behavior of solar irradiance data, a periodic autoregressive model was considered; the statistics were used by the maximum likelihood method, based on hourly measurements of irradiance over a period of one year.
Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.
Mixup Decoding for Diverse Machine Translation

Diverse machine translation aims at generating various target language translations for a given source language sentence.To leverage the linear relationship in the sentence latent space introduced by the mixup training, we propose a novel method, MixDiversity, to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding.To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly.Moreover, by controlling the interpolation weight, our method can achieve the trade-off between faithfulness and diversity without any additional training, which is required in most of the previous methods.Experiments on WMT'16 en→ro, WMT'14 en→de, and WMT'17 zh→en are conducted to show that our method substantially outperforms all previous diverse machine translation methods.
SaCI: A Blockchain-Based Cyber Insurance Approach for the Deployment and Management of a Contract Coverage

The cyber insurance market is still in its infancy but growing fast. Novel models and standards for this particular insurance market are essential due to the use of modern IT (Information Technology) and since insurance providers need to create suitable models for customers.In this work, a refreshing approach SaCI for the deployment and management of contract coverage is introduced. SaCI translates relevant information of a cyber insurance contract to Smart Contracts (SC) running on the Blockchain (BC). Thus, SaCI (i) allows for recording agreements in an immutable way, (ii) simplifies interactions between stakeholders (e.g., customers and insurers), and (iii) ensures a trustworthy and transparent process during the life-cycle of the contract. A case study is provided to show evidence of the feasibility of the approach, which is backed by a cost analysis and discussion regarding especially the application of BCs.
Faster than light (microscopy): superiority of digital pathology over microscopy for assessment of immunohistochemistry

Aims Digital pathology offers the potential for significant benefits in diagnostic pathology, but currently the efficiency of slide viewing is a barrier to adoption. We hypothesised that presenting digital slides for simultaneous viewing of multiple sections of tissue for comparison, as in those with immunohistochemical panels, would allow pathologists to review cases more quickly. Methods Novel software was developed to view synchronised parallel tissue sections on a digital pathology workstation. Sixteen histopathologists reviewed three liver biopsy cases including an immunohistochemical panel using the digital microscope, and three different liver biopsy cases including an immunohistochemical panel using the light microscope. The order of cases and interface was fully counterbalanced. Time to diagnosis was recorded and mean times are presented as data approximated to a normalised distribution. Results Mean time to diagnosis was 4 min 3 s using the digital microscope and 5 min 24 s using the light microscope, saving 1 min 21 s (95% CI 16 s to 2 min 26 s; p=0.02), using the digital microscope. Overall normalised mean time to diagnosis was 85% on the digital pathology workstation compared with 115% on the microscope, a relative reduction of 26%. Conclusions With appropriate interface design, it is quicker to review immunohistochemical slides using a digital microscope than the conventional light microscope, without incurring any major diagnostic errors. As digital pathology becomes more integrated with routine clinical workflow and pathologists increase their experience of the technology, it is anticipated that other tasks will also become more time-efficient.