Reviewerly/exHarmony huggingface dataset:
Card-Based Cryptographic Protocols with Malicious Players Using Private Operations

Abstract This paper shows new card-based cryptographic protocols using private operations that are secure against malicious players. Physical cards are used in card-based cryptographic protocols instead of computers. Operations that a player executes in a place where the other players cannot see are called private operations. Using several private operations, calculations of two variable Boolean functions and copy operations were realized with the minimum number of cards. Though private operations are very powerful in card-based cryptographic protocols, there is a problem that it is very hard to prevent malicious actions during private operations. Though most card-based protocols are discussed in the semi-honest model, there might be cases when the semi-honest model is not enough. Thus, this paper shows new protocols that are secure against malicious players. We show logical XOR, logical AND, n -variable Boolean function, and copy protocols. We can execute any logical computations with a combination of these protocols. We use envelopes as an additional tool that can be easily prepared and used by people.
A Study of Text Vectorization Method Combining Topic Model and Transfer Learning

With the development of Internet cloud technology, the scale of data is expanding. Traditional processing methods find it difficult to deal with the problem of information extraction of big data. Therefore, it is necessary to use machine-learning-assisted intelligent processing to extract information from data in order to solve the optimization problem in complex systems. There are many forms of data storage. Among them, text data is an important data type that directly reflects semantic information. Text vectorization is an important concept in natural language processing tasks. Because text data can not be directly used for model parameter training, it is necessary to vectorize the original text data and make it numerical, and then the feature extraction operation can be carried out. The traditional text digitization method is often realized by constructing a bag of words, but the vector generated by this method can not reflect the semantic relationship between words, and it also easily causes the problems of data sparsity and dimension explosion. Therefore, this paper proposes a text vectorization method combining a topic model and transfer learning. Firstly, the topic model is selected to model the text data and extract its keywords, to grasp the main information of the text data. Then, with the help of the bidirectional encoder representations from transformers (BERT) model, which belongs to the pretrained model, model transfer learning is carried out to generate vectors, which are applied to the calculation of similarity between texts. By setting up a comparative experiment, this method is compared with the traditional vectorization method. The experimental results show that the vector generated by the topic-modeling- and transfer-learning-based text vectorization (TTTV) proposed in this paper can obtain better results when calculating the similarity between texts with the same topic, which means that it can more accurately judge whether the contents of the given two texts belong to the same topic.
Artificial Intelligence in Medical Imaging—Learning From Past Mistakes in Mammography

Ashish Sarraju, MD; Dennis Bruemmer, MD, PhD; Erik Van Iterson, PhD; Leslie Cho, MD; Fatima Rodriguez, MD, MPH; Luke Laffin, MD
Sentiment analysis from Customer-generated online videos on product review using topic modeling and Multi-attention BLSTM

With the popularity of social websites and mobile applications including Instagram, YouTube, TikTok, etc., online videos shared by customers presenting their thoughts and reviews on products are posted daily in increasing numbers. Such online videos containing Voice of Customer (VOC) are precious for product designers or managers to capture customer sentiment and understand customer preference. For this purpose, we propose a novel method for analyzing customer sentiment from online videos on product review. Firstly, latent Dirichlet allocation (LDA) modeling is applied to identify the topics from the online videos after data preprocessing. Then sentiment polarity corresponding to each topic of each speaker in videos can be identified using our newly designed multi-attention bi-directional LSTM (BLSTM(MA)), which can better mine complex relationships among a speaker’s sentiments on different topics. This paper is of great practical value for company managers and researchers to better understand a large number of customer opinions on specific products. To explain the application of this method and prove its effectiveness, two cases respectively on smartphones and several published datasets are developed finally.
The “Fresnel Equations” for Diffuse radiation on Inclined photovoltaic Surfaces (FEDIS)

The well-known Fresnel equations solve for the reflection and transmission of light for precise incident angles. The transmission of diffuse radiation incident on a planar or domed surface is often needed for real-world applications. Due to the complexity of the Fresnel equations, the analytical solution of the integration has hitherto been unobtainable over the last centuries. Therefore, this problem was numerically solved by integrating the angular transmittances in space often leading to substantial computing burden and bias in the results. To efficiently estimate the solar energy resource for a glass-covered photovoltaic (PV) module, we derive an analytical solution of diffuse transmission based on the rigorous integration of an alternate form of the Fresnel equations. The approach leads to a simple yet accurate relative transmittance model that reconciles the solar energy sensed by pyranometers and PV panels. With limited and clearly stated approximations, the complex mathematical derivation resulted in an elegant solution. An experiment using 1-year of data at the National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) shows that the new model dramatically decreases the disparity between the solar radiation measurements by a Kipp and Zonen CM Pyranometer 22 (CMP22) and an IMT reference cell on a 1-axis tracking system. The solution in this paper can be widely used in scientific and engineering research, development, and applications wherever the Fresnel equations are used.
Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration

A major gap between few-shot and many-shot learning is the data distribution empirically oserved by the model during training. In few-shot learning, the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples, while the ground-truth data distribution is more accurately uncovered in many-shot learning to learn a well-generalized model. In this paper, we propose to calibrate the distribution of these few-sample classes to be more unbiased to alleviate such an over-fitting problem. The distribution calibration is achieved by transferring statistics from the classes with sufficient examples to those few-sample classes. After calibration, an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. Specifically, we assume every dimension in the feature representation from the same class follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Extensive experiments on three datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> ImageNet, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tiered</i> ImageNet, and CUB, show that a simple linear classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy by a large margin. Besides the favorable performance, the proposed method also exhibits high flexibility by showing consistent accuracy improvement when it is built on top of any off-the-shelf pretrained feature extractors and classification models without extra learnable parameters. The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation thus the generalization ability gain is convincing. We also establish a generalization error bound for the proposed distribution-calibration-based few-shot learning, which consists of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution assumption error</i> , the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution approximation error</i> , and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">estimation error</i> . This generalization error bound theoretically justifies the effectiveness of the proposed method.
xxAI - Beyond Explainable Artificial Intelligence

Abstract The success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, forestry, health, etc.) has therefore led to an demand for trust, which can be fostered if the methods can be interpreted and thus explained to humans. The research field of explainable artificial intelligence (XAI) provides the necessary foundations and methods. Historically, XAI has focused on the development of methods to explain the decisions and internal mechanisms of complex AI systems, with much initial research concentrating on explaining how convolutional neural networks produce image classification predictions by producing visualizations which highlight what input patterns are most influential in activating hidden units, or are most responsible for a model’s decision. In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering systems, structured explanations). In addition, we also intend to move beyond simply providing model explanations to directly improving the transparency, efficiency and generalization ability of models. We hope this volume presents not only exciting research developments in explainable AI but also a guide for what next areas to focus on within this fascinating and highly relevant research field as we enter the second decade of the deep learning revolution. This volume is an outcome of the ICML 2020 workshop on “XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.”
Unsupervised Face Detection in the Dark

Low-light face detection is challenging but critical for real-world applications, such as nighttime autonomous driving and city surveillance. Current face detection models rely on extensive annotations and lack generality and flexibility. In this paper, we explore how to learn face detectors without low-light annotations. Fully exploiting existing normal light data, we propose adapting face detectors from normal light to low light. This task is difficult because the gap between brightness and darkness is too large and complicated at the object level and pixel level. Accordingly, the performance of current low-light enhancement or adaptation methods is unsatisfactory. To solve this problem, we propose a joint High-Low Adaptation (HLA) framework. We design bidirectional low-level adaptation and multitask high-level adaptation. For low-level, we enhance the dark images and degrade the normal-light images, making both domains move toward each other. For high-level, we combine context-based and contrastive learning to comprehensively close the features on different domains. Experiments show that our HLA-Face v2 model obtains superior low-light face detection performance even without the use of low-light annotations. Moreover, our adaptation scheme can be extended to a wide range of applications, such as improving supervised learning and generic object detection. Project publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://daooshee.github.io/HLA-Face-v2-Website/</uri> .
User-Level Label Leakage from Gradients in Federated Learning

Abstract Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the very recent results on privacy risks when sharing gradients. Specifically, we investigate Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users’ training data from their shared gradients. The attack exploits the direction and magnitude of gradients to determine the presence or absence of any label. LLG is simple yet effective, capable of leaking potential sensitive information represented by labels, and scales well to arbitrary batch sizes and multiple classes. We mathematically and empirically demonstrate the validity of the attack under different settings. Moreover, empirical results show that LLG successfully extracts labels with high accuracy at the early stages of model training. We also discuss different defense mechanisms against such leakage. Our findings suggest that gradient compression is a practical technique to mitigate the attack.
A methodology for automated penetration testing of cloud applications

Security assessment is a very time- and money-consuming activity. It needs specialised security skills and, furthermore, it is not fully integrated into the software development life-cycle. One of the best solutions for the security testing of an application relies on the use of penetration testing techniques. Unfortunately, penetration testing is a typically human-driven procedure that requires a deep knowledge of the possible attacks to carry out and of the hacking tools that can be used to launch the tests. In this paper, we present a methodology that enables the automation of penetration testing techniques based on both application-level models, used to represent the application architecture and its security properties in terms of applicable threats, vulnerabilities and weaknesses, and on system-level models, adopted to automatically generate and execute the penetration testing activities. The proposed methodology can be easily integrated into a continuous integration development process and aid software developers in evaluating security.
Twitter Sentiment Analysis on Indian Government Project using R.

The main motive behind this research paper is to use the power of social media to observe, examine and analyze the opinion regarding recent Indian government project as the opinion of people plays a vital role in formulating the government policies. By getting into the deeper insights on social media, one can easily analyze the behavior of people regarding various issues and policies, which was otherwise impossible using traditional sources. The case study was done on Statue of Unity. Analysis was done on one of the famous social networking sites i.e. Twitter, using R programming language. Twitter API was used to collect the primary data. Tweets were analyzed by using opinion lexicon and Emotion lexicon-based approaches. Opinion Lexicon based approach categorized the sentiment of tweets in three categories, while Emotion Lexicon based approach refined them into eight more categories. The research work done in this paper will help government to understand the emotions of people regarding their policies and will also enrich people to help them understand majority vote of people.
Revisit Division Property Based Cube Attacks: Key-Recovery or Distinguishing Attacks?

Cube attacks are an important type of key recovery attacks against stream ciphers. In particular, they are shown to be powerful against Trivium-like ciphers. Traditional cube attacks are experimental attacks which could only exploit cubes of size less than 40. At CRYPTO 2017, division property based cube attacks were proposed by Todo et al., and an advantage of introducing the division property to cube attacks is that large cube sizes which are beyond the experimental range could be explored, and so powerful theoretical attacks were mounted on many lightweight stream ciphers.In this paper, we revisit the division property based cube attacks. There is an important assumption, called Weak Assumption, proposed in division property based cube attacks to support the effectiveness of key recovery. Todo et al. in CRYPTO 2017 said that the Weak Assumption was expected to hold for theoretically recovered superpolies of Trivium according to some experimental results on small cubes. In this paper, it is shown that the Weak Assumption often fails in cube attacks against Trivium, and moreover a new method to recover the exact superpoly of a given cube is developed based on the bit-based division property. With our method, for the cube I proposed by Todo et al. at CRYPTO 2017 to attack the 832-round Trivium, we recover its superpoly pI(x, v) = v68v78 · (x58⊕v70) · (x59x60⊕x34⊕x61). Furthermore, we prove that some best key recovery results given at CRYPTO 2018 on Trivium are actually distinguishing attacks. Hopefully this paper gives some new insights on accurately recovering the superpolies with the bit-based division property and also attract some attention on the validity of division property based cube attacks against stream ciphers.
Wireless Sensor Networks, Internet of Things, and Their Challenges

Internet of Things (IoT) is currently one of the top technological concepts where users and objects are interconnected using wired and wireless technologies such as Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE and Bluetooth, anywhere and anyplace. Within the past ten years, the idea of IoT has drawn massive consideration from both the business and research communities. Application domains may have many substantial benefits by means of an IoT approach. The idea of this field of study is to portray a basic knowledge of smart environmental monitoring system based on IoT. It has been stated in various studies in the past that IoT is facing multiple issues such as authentication, identification, availability, security and privacy, and socio-technical trust system (STTS). Nowadays, existing smart environments are continuing to face major IoT setbacks and challenges with regards to security, privacy, and STTS. Creating a STTS comparison in IoT is one of the principal significant breakthroughs necessary for building stable structures which will serve to eliminate doubt and technical setbacks. This study will present an outline of security, privacy and STTS in IoT while using a simulation method for comparing the results and justifying the outcomes. It aims to highlight and define the effectiveness of trust-management and how it should be exploited in IoT. The results will be indicated based upon past and present study result comparisons.
Advanced architectures for high-performance quantum networking

As practical quantum networks prepare to serve an ever-expanding number of nodes, there has grown a need for advanced auxiliary classical systems that support the quantum protocols and maintain compatibility with the existing fiber-optic infrastructure. We propose and demonstrate a quantum local area network design that addresses current deployment limitations in timing and security in a scalable fashion using commercial off-the-shelf components. We employ White Rabbit switches to synchronize three remote nodes with ultra-low timing jitter, significantly increasing the fidelities of the distributed entangled states over previous work with Global Positioning System clocks. Second, using a parallel quantum key distribution channel, we secure the classical communications needed for instrument control and data management. In this way, the conventional network which manages our entanglement network is secured using keys generated via an underlying quantum key distribution layer, preserving the integrity of the supporting systems and the relevant data in a future-proof fashion.
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that come from the geometric properties of embedding vectors, including hubness and isolation. To solve these geometric problems, many normalization approaches have been adopted for EA. However, the increasing scale of KGs renders it hard for EA models to adopt the normalization processes, thus limiting their usage in real-world applications. To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate. ClusterEA contains three components to align entities between large-scale KGs, including stochastic training, ClusterSampler, and SparseFusion. It first trains a large-scale Siamese GNN for EA in a stochastic fashion to produce entity embeddings. Based on the embeddings, a novel ClusterSampler strategy is proposed for sampling highly overlapped mini-batches. Finally, ClusterEA incorporates SparseFusion, which normalizes local and global similarity and then fuses all similarity matrices to obtain the final similarity matrix. Extensive experiments with real-life datasets on EA benchmarks offer insight into the proposed framework, and suggest that it is capable of outperforming the state-of-the-art scalable EA framework by up to 8 times in terms of Hits@1.
DIMMining

Graph mining, which finds specific patterns in the graph, is becoming increasingly important in various domains. We point out that accelerating graph mining suffers from the following challenges: (1) Heavy comparison for pruning: Pruning technique is widely used to reduce search space in graph mining. It applies constraints on vertex indices and involves massive index comparisons. (2) Low parallelism of set operations: The typical graph mining algorithms can be expressed as a series of set operations between neighbors of vertices, which suffer from low parallelism if vertices are streaming to the computation units. (3) Heavy data transfer: Graph mining needs to transfer intermediate data with two orders of magnitude larger than the original data volume between CPU and memory.