Reviewerly/exHarmony huggingface dataset:
Photonic convolutional accelerator and neural network in the Tera-OPs regime based on Kerr microcombs

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 TeraOPS (TOPS: operations per second), generating convolutions of images of 250,000 pixels with 8 bit resolution for 10 kernels simultaneously, enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real time video recognition.
Curse of Re-encryption: A Generic Power/EM Analysis on Post-Quantum KEMs

This paper presents a side-channel analysis (SCA) on key encapsulation mechanism (KEM) based on the Fujisaki–Okamoto (FO) transformation and its variants. The FO transformation has been widely used in actively securing KEMs from passively secure public key encryption (PKE), as it is employed in most of NIST post-quantum cryptography (PQC) candidates for KEM. The proposed attack exploits side-channel leakage during execution of a pseudorandom function (PRF) or pseudorandom number generator (PRG) in the re-encryption of KEM decapsulation as a plaintext-checking oracle that tells whether the PKE decryption result is equivalent to the reference plaintext. The generality and practicality of the plaintext-checking oracle allow the proposed attack to attain a full-key recovery of various KEMs when an active attack on the underlying PKE is known. This paper demonstrates that the proposed attack can be applied to most NIST PQC third-round KEM candidates, namely, Kyber, Saber, FrodoKEM, NTRU, NTRU Prime, HQC, BIKE, and SIKE (for BIKE, the proposed attack achieves a partial key recovery). The applicability to Classic McEliece is unclear because there is no known active attack on this cryptosystem. This paper also presents a side-channel distinguisher design based on deep learning (DL) for mounting the proposed attack on practical implementation without the use of a profiling device. The feasibility of the proposed attack is evaluated through experimental attacks on various PRF implementations (a SHAKE software, an AES software, an AES hardware, a bit-sliced masked AES software, and a masked AES hardware based on threshold implementation). Although it is difficult to implement the oracle using the leakage from the TI-based masked hardware, the success of the proposed attack against these implementations (even except for the masked hardware), which include masked software, confirms its practicality.
The Impact of SIPOC on Process Reengineering and Sustainability of Enterprise Procurement Management in E-Commerce Environments Using Deep Learning

In order to better promote the healthy and long-term development of enterprise procurement management process, under the background of e-commerce environment, Suppliers-Inputs-Process-Outputs-Customers (SIPOC) model, deep learning and related theories of enterprise procurement management are expounded and proposed. Then, D electric power enterprise is studied as samples. After understanding the current situation of procurement management of the enterprise, there are a series of problems in the enterprise, such as complex process, and no correlation between procurement management process and overall strategic planning. Finally, through the analysis of the early warning indicators of the enterprise by the deep learning algorithm, the procurement management process has caused certain risks to the financial management level of the enterprise, and the procurement management process of the enterprise needs to be adjusted. The material record and consumption scheme of the enterprise is optimized by using the SIPOC organizational system model.
Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model

It is undeniable that mobile devices have become an inseparable part of human’s daily routines due to the persistent growth of high-quality sensor devices, powerful computational resources and massive storage capacity nowadays. Similarly, the fast development of Internet of Things technology has motivated people into the research and wide applications of sensors, such as the human activity recognition system. This results in substantial existing works that have utilized wearable sensors to identify human activities with a variety of techniques. In this paper, a hybrid deep learning model that amalgamates a one-dimensional Convolutional Neural Network with a bidirectional long short-term memory (1D-CNN-BiLSTM) model is proposed for wearable sensor-based human activity recognition. The one-dimensional Convolutional Neural Network transforms the prominent information in the sensor time series data into high level representative features. Thereafter, the bidirectional long short-term memory encodes the long-range dependencies in the features by gating mechanisms. The performance evaluation reveals that the proposed 1D-CNN-BiLSTM outshines the existing methods with a recognition rate of 95.48% on the UCI-HAR dataset, 94.17% on the Motion Sense dataset and 100% on the Single Accelerometer dataset.
Peculiarities of using of the Moodle test tools in philosophy teaching

The paper considers the role of philosophy and philosophical disciplines as the means of forming general cultural competences, in particular, in the development of critical thinking. The article emphasizes that the process of forming over-subject and soft skills, which, as a rule, include also critical thinking, gets much more complicated under the conditions of the reduction in the volume of philosophical courses. The paper grounds that one of the ways to “return” philosophy to educational programmes can be the implementation of training, using the e-learning environment, especially Moodle. In addition, authors point to the expediency of using this system and, in general, e-learning as an instrument for collaborating students to the world’s educational community and for developing their lifelong learning skills. The article specifies the features of providing electronic support in philosophy teaching, to which the following belongs: the difficulty of parametrizing the learning outcomes; plurality of approaches; communicative philosophy. The paper highlights the types of activities that can be implemented by tools of Moodle. The use of the following Moodle test tasks is considered as an example: test control in the flipped class, control of work with primary sources, control of self-study, test implementation of interim thematic control. The authors conclude that the Moodle system can be used as a tools of online support for the philosophy course, but it is impossible to transfer to the virtual space all the study of this discipline, because it has a significant worldview load. Forms of training, directly related to communication, are integral part of the methodology of teaching philosophy as philosophy itself is discursive, dialogical, communicative and pluralistic. Nevertheless, taking into account features of the discipline, it is possible to provide not only the evaluation function of the test control, but also to realize a number of educational functions: updating the basic knowledge, memorization, activating the cognitive interest, developing the ability to reason and the simpler ones but not less important, – the skill of getting information and familiarization with it.
Convergence in Orlicz spaces by means of the multivariate max-product neural network operators of the Kantorovich type and applications

In this paper, convergence results in a multivariate setting have been proved for a family of neural network operators of the max-product type. In particular, the coefficients expressed by Kantorovich type means allow to treat the theory in the general frame of the Orlicz spaces, which includes as particular case the $L^p$-spaces. Examples of sigmoidal activation functions are discussed, for the above operators in different cases of Orlicz spaces. Finally, concrete applications to real world cases have been presented in both uni-variate and multivariate settings. In particular, the case of reconstruction and enhancement of biomedical (vascular) image has been discussed in details.
The lord of the shares

Secure cloud storage is considered one of the most important issues that both businesses and end-users are considering before moving their private data to the cloud. Lately, we have seen some interesting approaches that are based either on the promising concept of Symmetric Searchable Encryption (SSE) or on the well-studied field of Attribute-Based Encryption (ABE). In the first case, researchers are trying to design protocols where users' data will be protected from both internal and external attacks without paying the necessary attention to the problem of user revocation. On the other hand, in the second case existing approaches address the problem of revocation. However, the overall efficiency of these systems is compromised since the proposed protocols are solely based on ABE schemes and the size of the produced ciphertexts and the time required to decrypt grows with the complexity of the access formula. In this paper, we propose a protocol that combines both SSE and ABE in a way that the main advantages of each scheme are used. The proposed protocol allows users to directly search over encrypted data by using an SSE scheme while the corresponding symmetric key that is needed for the decryption is protected via a Ciphertext-Policy Attribute-Based Encryption scheme.
Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression

Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy. We employed tests of statistical significance to determine which genes were differentially expressed in the samples derived from patients who achieved CR after 2 courses of treatment and the samples taken from patients who did not benefit. We tuned classifier hyperparameters to optimize performance and used multiple methods to guide our feature selection as well as our assessment of algorithm performance. To identify the model which performed best within the context of this study, we plotted receiver operating characteristic (ROC) curves. Using the top 75 genes from the k-nearest neighbors algorithm (K-NN) model (K = 27) yielded the best area-under-the-curve (AUC) score that we obtained: 0.84. When we finally tested the previously unseen test data set, the top 50 genes yielded the best AUC = 0.81. Pathway enrichment analysis for these 50 genes showed that the guanosine diphosphate fucose (GDP-fucose) biosynthesis pathway is the most significant with an adjusted P value = .0092, which may suggest the vital role of N-glycosylation in AML.
LPI Radar Waveform Recognition Based on CNN and TPOT

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.
Two and three-uniform states from irredundant orthogonal arrays

Abstract A pure quantum state of N subsystems, each with d levels, is said to be k -uniform if all of its reductions to k qudits are maximally mixed. Only the uniform states obtained from orthogonal arrays (OAs) are considered throughout this work. The Hamming distances of OAs are specially applied to the theory of quantum information. By using difference schemes and orthogonal partitions, we construct a series of infinite classes of irredundant orthogonal arrays (IrOAs), then answer the open questions of whether there exist 3-uniform states of N qubits and 2-uniform states of N qutrits, and whether 3-uniform states of qudits ( d &gt; 2) for high values of N can be explicitly constructed. In fact, we obtain 3-uniform states for an arbitrary number of N ≥ 8 qubits and 2-uniform states of N qutrits for every N ≥ 4. Additionally, we provide explicit constructions of the 3-uniform states of N ≥ 8 qutrits, N = 6 and N ≥ 8 ququarts and ququints, N ≥ 6 qudits having d levels for any prime power d &gt; 6, and N = 8 and N ≥ 12 qudits having d levels for non-prime-power d ≥ 6. Moreover, we describe an explicit construction scheme for the 2-uniform states of qudits having d ≥ 4 levels. The proofs of existence of the 2-uniform states of N ≥ 6 qubits are simplified by using a class of OAs. Two special 3-uniform states are obtained from IrOA(32, 10, 2, 3) and IrOA(32, 11, 2, 3) using the interaction column property of OAs.
Efficient Meta Learning via Minibatch Proximal Update

We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks. A particularly simple yet successful paradigm for this research is model-agnostic meta-learning (MAML). Implementation and analysis of MAML, however, can be tricky; first-order approximation is usually adopted to avoid directly computing Hessian matrix but as a result the convergence and generalization guarantees remain largely mysterious for MAML. To remedy this deficiency, in this paper we propose a minibatch proximal update based meta-learning approach for learning to efficient hypothesis transfer. The principle is to learn a prior hypothesis shared across tasks such that the minibatch risk minimization biased regularized by this prior can quickly converge to the optimal hypothesis in each training task. The prior hypothesis training model can be efficiently optimized via SGD with provable convergence guarantees for both convex and non-convex problems. Moreover, we theoretically justify the benefit of the learnt prior hypothesis for fast adaptation to new few-shot learning tasks via minibatch proximal update. Experimental results on several few-shot regression and classification tasks demonstrate the advantages of our method over state-of-the-arts.
Reading Like HER: Human Reading Inspired Extractive Summarization

Ling Luo, Xiang Ao, Yan Song, Feiyang Pan, Min Yang, Qing He. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Photo-Chromeleon

In this paper, we present a method to create re-programmable multi-color textures that are made from a single material only. The key idea builds on the use of photochromic inks that can switch their appearance from transparent to colored when exposed to light of a certain wavelength. By mixing cyan, magenta, and yellow (CMY) photochromic dyes into a single solution and leveraging the different absorption spectra of each dye, we can control each color channel in the solution separately. Our approach can transform single-material fabrication techniques, such as coating, into high-resolution multi-color processes. We discuss the material mixing procedure, modifications to the light source, and the algorithm to control each color channel. We then show the results from an experiment in which we evaluated the available color space and the resolution of our textures. Finally, we demonstrate our user interface that allows users to transfer virtual textures onto physical objects and show a range of application examples.
Semantic Segmentation for Prohibited Items in Baggage Inspection

The X-ray screening system is crucial to protecting the safety of public spaces. However, automated detection in baggage inspection is still far from practical application. Most detection tasks rely mainly on humans. In this paper, the detection of prohibited items is regarded as a semantic segmentation task. Considering some characters of security imageries, we propose a segmentation net with novel dual attention, which could capture richer features for refining the segmentation results. Our model could not only automatically recognize the class of prohibited items but also locate prohibited items in baggage. It could facilitate the security staffs to carry out inspection. To validate the effectiveness of our proposed model, extensive experiments have been conducted on the real X-ray security imageries datasets. The experimental results show the net achieves super performance (mIoU of 0.683).
Accelerating Fully Homomorphic Encryption Through Architecture-Centric Analysis and Optimization

Homomorphic Encryption (HE) draws a significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous HE schemes proposed, HE for Arithmetic of Approximate Numbers (HEAAN) is rapidly gaining popularity across a wide range of applications because it supports messages that can tolerate approximate computation with no limit on the number of arithmetic operations applicable to the corresponding ciphertexts. A critical shortcoming of HE is the high computation complexity of ciphertext arithmetic; especially, HE multiplication (HE Mul) is more than 10,000 times slower than the corresponding multiplication between unencrypted messages. This leads to a large body of HE acceleration studies, including ones exploiting FPGAs; however, those did not conduct a rigorous analysis of computational complexity and data access patterns of HE Mul. Moreover, the proposals mostly focused on designs with small parameter sizes, making it difficult to accurately estimate their performance in conducting a series of complex arithmetic operations. In this paper, we first describe how HE Mul of HEAAN is performed in a manner friendly to computer architects. Then we conduct a disciplined analysis on its computational and memory access characteristics, through which we (1) extract parallelism in the key functions composing HE Mul and (2) demonstrate how to effectively map the parallelism to the popular parallel processing platforms, multicore CPUs and GPUs, by applying a series of optimization techniques such as transposing matrices and pinning data to threads. This leads to the performance improvement of HE Mul on a CPU and a GPU by 42.9x and 134.1x, respectively, over the single-thread reference HEAAN running on a CPU. The conducted analysis and optimization would set a new foundation for future HE acceleration research.
Robust and Fast Holonomic Quantum Gates with Encoding on Superconducting Circuits

High-fidelity and robust quantum manipulation is the key for scalable quantum computation. Therefore, due to the intrinsic operational robustness, quantum manipulation induced by geometric phases is one of the promising candidates. However, the longer gate time for geometric operations and more physical-implementation difficulties hinder its practical and wide applications. Here, we propose a simplified implementation of universal holonomic quantum gates on superconducting circuits with experimentally demonstrated techniques, which can remove the two main challenges by introducing the time-optimal control into the construction of quantum gates. Remarkably, our scheme is also based on a decoherence-free subspace encoding, with minimal physical qubit resource, which can further immune to error caused by qubit-frequency drift, which is regarded as the main error source for large scale superconducting circuits. Meanwhile, we deliberately design the quantum evolution to eliminate gate error caused by unwanted leakage sources. Therefore, our scheme is more robust than the conventional ones, and thus provides a promising alternative strategy for scalable fault-tolerant quantum computation.
Friendly Topic Assistant for Transformer Based Abstractive Summarization

ive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance. Compared with Transformers, topic models are better at learning explicit document semantics, and hence could be integrated into Transformers to further boost their performance. To this end, we rearrange and explore the semantics learned by a topic model, and then propose a topic assistant (TA) including three modules. TA is compatible with various Transformer-based models and user-friendly since i) TA is a plug-and-play model that does not break any structure of the original Transformer network, making users easily fine-tune Transformer+TA based on a well pre-trained model; ii) TA only introduces a small number of extra parameters. Experimental results on three datasets demonstrate that TA is able to improve the performance of several Transformer-based models.
merlin—A unified modeling framework for data analysis and methods development in Stata

The challenges in statistics and data science are rapidly growing because access to a multitude of data types continues to increase, as well as the sheer quantity of data. Analysts are now presented with multivariate data, sometimes measured repeatedly, and often requiring the ability to model nonlinear relationships and hierarchical structures. In this article, I present the merlin command, which attempts to provide an extremely general framework for data analysis. From simple settings such as fitting a linear regression model or a Weibull survival model to more complex settings such as fitting a three-level logistic mixed-effects model or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with nonlinear effects, merlin can fit them all. I will take a single dataset and attempt to show you the full range of capabilities of merlin and discuss some future directions for the implementation in Stata.