Reviewerly/exHarmony huggingface dataset:
An Efficient Architecture and High-Throughput Implementation of CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA Technology

Nowadays, hyperspectral imaging is recognized as cornerstone remote sensing technology. The explosive growth in image data volume and instrument data rates, compete with limited on-board storage and downlink bandwidth, making hyperspectral data compression a mission critical task. Recently, the consultative committee for space data systems (CCSDS) extended the previous issue of the CCSDS-123.0 Recommended Standard for multi/hyperspectral image compression to provide Near-Lossless compression functionality. A key feature of the CCSDS-123.0-B-2 is the improved Hybrid Entropy Coder providing substantially better compression performance than the Issue 1 entropy coders at low bit rates. In this paper, we introduce a high-throughput hardware implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder. The introduced architecture exploits the systolic design pattern providing modularity and latency insensitivity in a deep and elastic pipeline, achieving constant throughput of 1 sample/cycle with small field programmable gate array (FPGA) resource footprint. This architecture is described in portable VHDL register-transfer level (RTL) and implemented, validated and demonstrated on a commercially available Xilinx KCU105 development board hosting a Xilinx Kintex Ultrascale XCKU040 SRAM FPGA, and thus, is directly transferable to the Xilinx Radiation Tolerant Kintex UltraScale XQRKU060 space-grade devices. Moreover, state-of-the-art SpaceFibre (ECSS-E-ST-50-11C) serial link interface and test equipment were used in the validation platform to emulate an on-board deployment. The introduced CCSDS-123.0-B-2 Hybrid Entropy Coder achieves a constant throughput performance of 305 MSamples/s. To the best of our knowledge, this is the first published fully-compliant architecture and high-throughput implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder, targeting space-grade FPGA technology.
Verifying Neural Networks Against Backdoor Attacks

Abstract Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is known to significantly reduce neural network performance. In this work, we propose an approach to verify whether a given neural network is free of backdoor with a certain level of success rate. Our approach integrates statistical sampling as well as abstract interpretation. The experiment results show that our approach effectively verifies the absence of backdoor or generates backdoor triggers.
Suppressing classical noise in the accelerated geometric phase gate by optimized dynamical decoupling

In the quantum-computation scenario, the geometric phase gates are becoming increasingly attractive for their intrinsic fault tolerance to disturbance. With an adiabatic cyclic evolution, Berry phase appears to realize a geometric transformation. Performing the quantum gates as many as possible within the timescale of coherence, however, remains an inconvenient bottleneck due to the systematic errors. Here we propose an accelerated adiabatic quantum gate based on the Berry phase, the transitionless driving, and the dynamical decoupling. It reconciles a high fidelity with a high speed in the presence of control noise or imperfection. We optimize the dynamical-decoupling sequence in the time domain under a popular Gaussian noise spectrum following the inversely quadratic power law.
ANNA: Specialized Architecture for Approximate Nearest Neighbor Search

Similarity search or nearest neighbor search is a task of retrieving a set of vectors in the (vector) database that are most similar to the provided query vector. It has been a key kernel for many applications for a long time. However, it is becoming especially more important in recent days as modern neural networks and machine learning models represent the semantics of images, videos, and documents as high-dimensional vectors called embeddings. Finding a set of similar embeddings for the provided query embedding is now the critical operation for modern recommender systems and semantic search engines. Since exhaustively searching for the most similar vectors out of billion vectors is such a prohibitive task, approximate nearest neighbor search (ANNS) is often utilized in many real-world use cases. Unfortunately, we find that utilizing the server-class CPUs and GPUs for the ANNS task leads to suboptimal performance and energy efficiency. To address such limitations, we propose a specialized architecture named ANNA (Approximate Nearest Neighbor search Accelerator), which is compatible with state-of-the-art ANNS algorithms such as Google ScaNN and Facebook Faiss. By combining the benefits of a specialized dataflow pipeline and efficient data reuse, ANNA achieves multiple orders of magnitude higher energy efficiency, 2.3-61.6&#x00D7; higher throughput, and 4.3-82.1&#x00D7; lower latency than the conventional CPU or GPU for both million- and billion-scale datasets.
Collective Relevance Labeling for Passage Retrieval

Deep learning for Information Retrieval (IR) requires a large amount of high-quality querydocument relevance labels, but such labels are inherently sparse.Label smoothing redistributes some observed probability mass over unobserved instances, often uniformly, uninformed of the true distribution.In contrast, we propose knowledge distillation for informed labeling, without incurring high computation overheads at evaluation time.Our contribution is designing a simple but efficient teacher model which utilizes collective knowledge, to outperform state-of-thearts distilled from a more complex teacher model.Specifically, we train up to ×8 faster than the state-of-the-art teacher, while distilling the rankings better.
Blind Speech Watermarking Method with Frame Self-Synchronization Based on Spread-Spectrum Using Linear Prediction Residue

State-of-the-art speech watermarking techniques enable speech signals to be authenticated and protected against any malicious attack to ensure secure speech communication. In general, reliable speech watermarking methods must satisfy four requirements: inaudibility, robustness, blind-detectability, and confidentiality. We previously proposed a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction (LP) scheme to solve the first two issues (inaudibility and robustness) due to distortion by spread spectrum. This method not only effectively embeds watermarks with small distortion but also has the same robustness as the DSS method. There are, however, two remaining issues with blind-detectability and confidentiality. In this work, we attempt to resolve these issues by developing an approach called the LP-DSS scheme, which takes two forms of data embedding for blind detection and frame synchronization. We incorporate blind detection with frame synchronization into the scheme to satisfy blind-detectability and incorporate two forms of data embedding process, front-side and back-side embedding for blind detection and frame synchronization, to satisfy confidentiality. We evaluated these improved processes by carrying out four objective tests (PESQ, LSD, Bit-error-rate, and accuracy of frame synchronization) to determine whether inaudibility and blind-detectability could be satisfied. We also evaluated all combinations with the two forms of data embedding for blind detection with frame synchronization by carrying out BER tests to determine whether confidentiality could be satisfied. Finally, we comparatively evaluated the proposed method by carrying out ten robustness tests against various processing and attacks. Our findings showed that an inaudible, robust, blindly detectable, and confidential speech watermarking method based on the proposed LP-DSS scheme could be achieved.
Comparative Analysis of GMM, KNN, and ViBe Background Subtraction Algorithms Applied in Dynamic Background Scenes of Video Surveillance System

Implementing and comparing the results of GMM, KNN, and ViBe background subtraction algorithms.• Applying algorithms on dynamic background scenes from a well-known CDnet 2014 benchmark dataset.• A wide range of evaluation metrics has been used (Accuracy, Precession, Recall, F1, FPR, FNR, and PWC).• ViBe background subtraction algorithm shows the best overall performance.Background subtraction is the most prominent technique applied in the domain of detecting moving objects.However, there is a wide range of different background subtraction models.Choosing the best model that addresses a number of challenges is still a vital research area.Therefore, in this article we present a comparative analysis of three promising algorithms used in this domain, GMM, KNN and ViBe.CDnet 2014 is the benchmark dataset used in this analysis with several quantitative evaluation metrics like precession, recall, f-measures, false positive rate, false negative rate and PWC.In addition, qualitative evaluations are illustrated in snapshots to depict the visual scenes evaluation.ViBe algorithm outperform other algorithms for overall evaluations.
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias

In this paper, we revisit the solving bias when evaluating models on current Math Word Problem (MWP) benchmarks. However, current solvers exist solving bias which consists of data bias and learning bias due to biased dataset and improper training strategy. Our experiments verify MWP solvers are easy to be biased by the biased training datasets which do not cover diverse questions for each problem narrative of all MWPs, thus a solver can only learn shallow heuristics rather than deep semantics for understanding problems. Besides, an MWP can be naturally solved by multiple equivalent equations while current datasets take only one of the equivalent equations as ground truth, forcing the model to match the labeled ground truth and ignoring other equivalent equations. Here, we first introduce a novel MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in our collected data and annotating them with corresponding multiple new questions manually. Then, to further mitigate learning bias, we propose a Dynamic Target Selection (DTS) Strategy to dynamically select more suitable target expressions according to the longest prefix match between the current model output and candidate equivalent equations which are obtained by applying commutative law during training. The results show that our UnbiasedMWP has significantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers' reasoning skills rather than matching nearest neighbors. And the solvers trained with our DTS achieve higher accuracies on multiple MWP benchmarks. The source code is available at https://github.com/yangzhch6/UnbiasedMWP.
Color image encryption using vectorial light field through a compact optical set-up

Abstract Vector light fields have spatially non-uniform polarization across the transverse plane. In this study, a color image encryption scheme has been proposed in which the data is encoded into the spatially varying polarization states of light to achieve higher degrees of freedom. This scheme provides a strong security and a straightforward way for optical implementation. Simulation and experimental results have been presented to verify the feasibility of the proposed scheme.
Personalized Hybrid Education Framework Based on Neuroevolution Methodologies

The future pedagogical systems need anthropocentric inclusive educational programs in which the goal should be adjustable according to the knowledge requirements, intelligence, and learning objective of each student. Prioritizing these needs, innovative AI methods are required to assist and ensure the making of conscious educational decisions, in terms of clear identification and categorization with high accuracy of various forms of skills and knowledge of each student. This paper proposes a neuroevolution emerging technique that combines the searchability of evolutionary computation and the learning capability of a hybrid artificial neural networks method. Specifically, the proposed growing semiorganizing neural gas (GsONG) is a practical AI methodology utilizing advanced clustering techniques to enhance the learning experience by categorizing the true abilities, skills, and needs of learners, in an inclusive differentiated learning framework. It is a neural network architecture that includes competing and cooperating neurons with an unstructured mode whereby a cooperation-competition process delimits the topological neighborhood of neurons in a grid to identify patterns for which their classes are not known. To optimize the above process, a heuristic method was used that investigates the space of an objective function by regulating the optimal topologies of neurons that form pathway segments in a semi-contemplative manner. Based on the extensive experiments and results obtained from the GsONG clustering approach, the proposed algorithm can compensate with high accuracy for difficulties in multicriteria grouping and differentiation of uncertainty structures such as in small or tiny data sets.
Unsupervised English Intelligent Machine Translation in Wireless Network Environment

Researchers suggest unsupervised English machine translation to address the absence of parallel corpus in English translation. Unsupervised pretraining techniques, denoising autoencoders, back translation, and shared latent representation mechanisms are used to simulate the translation task using just monolingual corpora. This paper uses pseudo-parallel data to construct unsupervised neural machine translation (NMT) and dissimilar language pair analysis. This paper firstly analyzes the low performance of unsupervised translation on dissimilar language pairs from three aspects: bilingual word embedding quality, shared words, and word order. And artificial shared word replacement and preordering strategies are proposed to increase the shared words between dissimilar language pairs and reduce the difference in their syntactic structure, thereby improving the translation performance on dissimilar language pairs. The denoising autoencoder and shared latent representation mechanism in unsupervised English machine translation are only required in the early stage of training, and learning the shared latent representation limits the further improvement of performance in different directions. While training the denoising autoencoder by repeatedly altering the training data slows down the convergence of the model, this is especially true for divergent languages. This paper presents an unsupervised NMT model based on pseudo-parallel data to address this issue. It trains two standard supervised neural machine translation models using the pseudo-parallel corpus generated by the unsupervised neural machine translation system, which enhances translation performance and speeds convergence. Finally, the English intelligent translation model is deployed in the wireless network server, and users can access it through the wireless network.
Mode and Effect Evaluation of Classroom Regulation Under Hybrid Teaching Mode in Colleges

Under hybrid teaching mode, teachers often find it hard to control the rhythm of the classroom, and need to sort out the relationship between various elements in the class. To achieve the ideal teaching effect through classroom regulation, this paper explores the mode and effect evaluation of classroom regulation under hybrid teaching mode in colleges. Firstly, the authors preprocessed and extracted features from the data on classroom regulation indices, and explained the training process and steps of the proposed backpropagation neural network (BPNN). Based on fuzzy comprehensive evaluation (FCE), a state evaluation model was established for classroom regulation, and the construction procedure was detailed for the multi-level FCE model. Next, an index system was constructed for the effect evaluation of classroom regulation under hybrid teaching mode in colleges, and the evaluation process was clarified for the classroom regulation effect. Finally, several experiments were carried out to examine the implementation of classroom regulation measures in different periods, and obtain the evaluated effects of classroom regulation.
Evolving SimGANs to improve abnormal electrocardiogram classification

Machine Learning models are used in a wide variety of domains. However, machine learning methods often require a large amount of data in order to be successful. This is especially troublesome in domains where collecting real-world data is difficult and/or expensive. Data simulators do exist for many of these domains, but they do not sufficiently reflect the real world data due to factors such as a lack of real-world noise. Recently generative adversarial networks (GANs) have been modified to refine simulated image data into data that better fits the real world distribution, using the SimGAN method. While evolutionary computing has been used for GAN evolution, there are currently no frameworks that can evolve a SimGAN. In this paper we (1) extend the SimGAN method to refine one-dimensional data, (2) modify Easy Cartesian Genetic Programming (ezCGP), an evolutionary computing framework, to create SimGANs that more accurately refine simulated data, and (3) create new feature-based quantitative metrics to evaluate refined data. We also use our framework to augment an electrocardiogram (ECG) dataset, a domain that suffers from the issues previously mentioned. In particular, while healthy ECGs can be simulated there are no current simulators of abnormal ECGs. We show that by using an evolved SimGAN to refine simulated healthy ECG data to mimic real-world abnormal ECGs, we can improve the accuracy of abnormal ECG classifiers.
Tower: data structures in Quantum superposition

Emerging quantum algorithms for problems such as element distinctness, subset sum, and closest pair demonstrate computational advantages by relying on abstract data structures. Practically realizing such an algorithm as a program for a quantum computer requires an efficient implementation of the data structure whose operations correspond to unitary operators that manipulate quantum superpositions of data. To correctly operate in superposition, an implementation must satisfy three properties --- reversibility, history independence, and bounded-time execution. Standard implementations, such as the representation of an abstract set as a hash table, fail these properties, calling for tools to develop specialized implementations. In this work, we present Core Tower, the first language for quantum programming with random-access memory. Core Tower enables the developer to implement data structures as pointer-based, linked data. It features a reversible semantics enabling every valid program to be translated to a unitary quantum circuit. We present Boson, the first memory allocator that supports reversible, history-independent, and constant-time dynamic memory allocation in quantum superposition. We also present Tower, a language for quantum programming with recursively defined data structures. Tower features a type system that bounds all recursion using classical parameters as is necessary for a program to execute on a quantum computer. Using Tower, we implement Ground, the first quantum library of data structures, including lists, stacks, queues, strings, and sets. We provide the first executable implementation of sets that satisfies all three mandated properties of reversibility, history independence, and bounded-time execution.
ZoomShop: Depth‐Aware Editing of Photographic Composition

Abstract We present ZoomShop, a photographic composition editing tool for adjusting relative size, position, and foreshortening of scene elements. Given an image and corresponding depth map as input, ZoomShop combines a novel non‐linear camera model and a depth‐aware image warp to reproject and deform the image. Users can isolate objects by selecting depth ranges and adjust their scale and foreshortening, which controls the paths of the camera rays through the scene. Users can also select 2D image regions and translate them, which determines the objective function in the image warp optimization. We demonstrate that ZoomShop can be used to achieve useful compositional goals, such as making a distant object more prominent while preserving foreground scenery, or making objects both larger and closer together so they still fit in the frame.
Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers

Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains.
A Dataset for Sentence Retrieval for Open-Ended Dialogues

We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conversational search. To address a broader scope of this task where any type of dialogue can be used, we constructed a dataset that includes open-ended dialogues from Reddit, candidate sentences from Wikipedia for each dialogue and human annotations for the sentences. We report the performance of several retrieval baselines, including neural retrieval models, over the dataset. To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. Using this training set significantly improved the performance over training on the MS MARCO dataset.