Reviewerly/exHarmony huggingface dataset:
Succinct Scriptable NIZK via Trusted Hardware

Non-interactive zero-knowledge proof or argument (NIZK) systems are widely used in many security sensitive applications to enhance computation integrity, privacy and scalability. In such systems, a prover wants to convince one or more verifiers that the result of a public function is correctly computed without revealing the (potential) private input, such as the witness. In this work, we introduce a new notion, called succinct scriptable NIZK, where the prover and verifier(s) can specify the function (or language instance) to be proven via a script. We formalize this notion is UC framework and provide a generic trusted hardware based solution. We then instantiate our solution in both SGX and Trustzone with Lua script engine. The system can be easily used by typical programmers without any cryptographic background. The benchmark result shows that our solution is better than all the known NIZK proof systems w.r.t. prover’s running time (1000 times faster), verifier’s running time, and the proof size. Finally, we show how the proposed scriptable succinct NIZK can be readily deployed to solve many well-known problems in the blockchain context, e.g. verifier’s dilemma, fast joining for new players, etc..
Learnable Sparse Transform Siamese Attention Networks for Visual Tracking

Visual tracking based on Siamese Network formulates the tracking task as a matching problem between the target template and search regions. Most existing algorithms include offline training and online tracking stage, respectively. The corresponding deep features are extracted by Convolutional Neural Network (CNN). In these tracking algorithms, the spatial and channel-wise redundancy of features usually remain during the feature extraction, which limits the performance of the tracker. In this paper, we propose a Learnable Sparse Transform Siamese Networks based tracking algorithm, referred to as SiamLST, which consists of a feature extraction network with shared weights, a learnable sparse transformation model and a crosscorrelation operation for similarity matching. By transforming the features map into sparser domain, we obtain richness features map and reduce the redundancy of local features. In addition, the SiamLST has better ability to represent a target in complex situations, such as image noise or corruptions at the inference stage. A large number of experiments on OTB2015 illustrate that the proposed algorithm achieve excellent tracking performances.
Stochastic Temporal Difference Learning for Sequence Data

Planning is crucial to train an agent via model-based reinforcement learning who can predict distant observations to reflect his/her past experience. Such a planning method is theoretically and computationally attractive in comparison with traditional learning which relies on step-by-step prediction. However, it is more challenging to build a learning machine which can predict and plan randomly across multiple time steps rather than act step by step. To reflect this flexibility in learning process, we need to predict future states directly without going through all intermediate states. Accordingly, this paper develops the stochastic temporal difference learning where the sequence data are represented with multiple jumpy states while the stochastic state space model is learned by maximizing the evidence lower bound of log likelihood of training data. A general solution with various number of jumpy states is developed and formulated. Experiments demonstrate the merit of the proposed sequential machine to find predictive states to roll forward with jumps as well as predict words.
Toward Fine-Grained Sleeping Activity Recognition: 3d Extension and an Estimation Try on Joint Position of SLP Dataset

Sleeping posture estimation including joint positions is important for identifying pressure ulcer risk. Since there are occlusion and privacy problems with camera images, we have been studying 2D joint position estimation from sleeping posture pressure images. However, the 2D joint position does not reveal 3d relationships among body parts, such as crossing legs. Thus, toward fine-grained sleeping activity recognition, 3D joint position estimation is required. To study it, we extend the 2D joint-position data of SLP dataset for 3D and then tried to estimate them with high accuracy. In this paper, described the details of the 3D extension and an estimation result. With one network and two loss extensions for a 2D to 3D joint position estimation network, we achieved 6.909 ± 0.278 cm accuracy assuming the average skeleton of Japanese, with about 42.5% error reduction by the extensions.
Optimization of sensor distribution using Gaussian processes

Experiments are undertaken both to test a hypothesis and, as is usually the case in metrology, to estimate the values of quantities. Experimental design is the process of planning the experiment in order to ensure that measurements gathered are of the right type and are of sufficient quality and quantity to achieve the aims of the experiment as efficiently as possible. In this paper, we discuss the design of experiments for systems modelled in terms of Gaussian Processes that account for spatially (and/or temporally) correlated random effects. In particular, we look at how the number of measurements required depends on the degree of spatial/temporal correlation. These methods have been applied to determine the placement of a network of sensors on a thermal plate in order to determine the heat profile of the plate to sufficient accuracy with as few sensors as possible.
Sequential Gaussian Process Regression for Simultaneous Pathology Detection and Shape Reconstruction

In this paper, we view pathology segmentation as an outlier detection task. Hence no prior on pathology characteristics is needed, and we can rely solely on a statistical prior on healthy data. Our method is based on the predictive posterior distribution obtained through standard Gaussian process regression. We propose a region-growing strategy, where we incrementally condition a Gaussian Process Morphable Model on the part considered healthy, as well as a dynamic threshold, which we infer from the uncertainty remaining in the resulting predictive posterior distribution. The threshold is used to extend the region considered healthy, which in turn is used to improve the regression results. Our method can be used for detecting missing parts or pathological growth like tumors on a target shape. We show segmentation results on a range of target surfaces: mandible, cranium and kidneys. The algorithm itself is theoretically sound, straight-forward to implement and extendable to other domains such as intensity-based pathologies. Our implementation is made open source with the publication.
Recognition Analysis and Simulation Implementation Based on High-Order Cumulants of Wireless Digital Modulation Mode

This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal.Set the identification signal modulation type determination threshold based on the value of the identification feature parameter.The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal.This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal.The second, fourth and sixth order cumulants of the six signals were analyzed.Calculate the selected identification feature parameter value and the determination threshold to identify the six signals.The six signals have made MATLAB identification simulation.Simulation results show that this method is feasible and has high recognition rate.Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio.This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.
Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

As a means of regulating people’s code of conduct, law has a close relationship with text, and text data has been growing exponentially. Managing and classifying huge text data have become a huge challenge. The PDES image segmentation algorithm is an effective natural language processing method for text classification management. Based on the study of image segmentation algorithm and legal case text classification theory, an image segmentation model based on partial differential equation is proposed, in which diffusion indirectly acts on level set function through auxiliary function. The software architecture of image segmentation algorithm text classification system is proposed by using computer technology and three-layer architecture model, which can improve the classification ability of text classification algorithm. The validity of pDE image segmentation model is verified by experiments. The experimental results show that the model completes the legal case text classification, the performance of each functional module of the legal case text classification system is good, and the efficiency and quality of the legal case text classification are improved.
H-GAN: the power of GANs in your Hands

We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach implementing multi-scale perceptual discriminators. It is designed to translate synthetic images of hands to the real domain. Synthetic hands provide complete ground-truth annotations, yet they are not representative of the target distribution of real-world data. We strive to provide the perfect blend of a realistic hand appearance with synthetic annotations. Relying on image-to-image translation, we improve the appearance of synthetic hands to approximate the statistical distribution underlying a collection of real images of hands. H-GAN tackles not only the cross-domain tone mapping but also structural differences in localized areas such as shading discontinuities. Results are evaluated on a qualitative and quantitative basis improving previous works. Furthermore, we relied on the hand classification task to claim our generated hands are statistically similar to the real domain of hands.
A Comparative Analysis of Expert Opinions on Artificial Intelligence: Evolution, Applications, and Its Future

Artificial Intelligence (AI) is a field of computer science that primarily focuses on automating tasks that explicitly require human intelligence. The mechanics of AI technology majorly revolves around central affairs including knowledge representation, learning, problem-solving, reasoning, etc. Additionally, each discipline of AI focuses on a particular component to efficiently train the machines. Every branch of AI technology exploits knowledge in machines using diversified practices but with a clear idea of achieving the desired output. AI has evolved drastically over the past two decades and is considered the most in-demand technology at present times in varied fields including healthcare, education, forecasting, security, etc. This paper provides an extensive survey on artificial intelligence and related work going on in this field, how it differs from human intelligence, various subfields of AI and their importance, various issues related to AI and possible solutions along with and future trends related to this technology depicting people’s reliability on it and various possible concerns.
Plug-and-Play ADMM Using Deep Image Prior for Image Denoising

As a classical image processing problem, image denoising arms to estimate the original image from the noisy version according to the prior conditions. Recent studies have shown that deep neural networks can also be seen as an image prior, referring to the Deep Image Prior (DIP) technique. The DIP method is able to recover a natural image only using random inputs and the corrupted observation through performing correction via a convolutional network. In this paper, we propose to improve the performance of the DIP method. Firstly, the original optimization objective function is modified by adding two nonlocal regularizers to promote sparsity of the solution. Secondly, we solve the optimization problem with the Alternating Direction Method of Multipliers (ADMM) framework, leading to a plug-and-play ADMM method for deep learning-based image denoising. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of our method both quantitatively and visually as compared with the original DIP method.
Human Learning Optimization with Self-tuning Random Learning Strategy

The human learning optimization (HLO) algorithm generates new solutions and searches for optimal solutions through random learning operator (RLO), individual learning operator (ILO) and social learning operator (SLO), of which the RLO is important for the exploration and exploitation abilities of HLO. To achieve an ideal balance between the exploration and exploitation and improve the search efficiency of the algorithm, this paper proposes an improved human learning optimization algorithm with self-tuning random learning strategy (HLOSRL), which enables the algorithm to automatically adjust the probability of RLO according to the feedback information. The proposed HLOSRL is compared with previous HLO variants as well as other recent meta-heuristics. The experimental results show that the HLOSRL has a better global searching ability.
A data hiding scheme based on single pixel modification with modulus operation

The embedding capacity and steganography quality are two important performance indicators of data hiding which has practical application value for copyright and intellectual property protection, public information protection and online elections. Many researches presented hiding methods to improve the performance. However, the existing data hiding methods have problems such as low embedding capacity or poor stego-image quality. This paper proposes a new method (Single Pixel Modification, SPM) to improve the performance further. The SPM (Single Pixel Modification) method embeds k secret bits into a cover-pixel with the idea that minimizing the change to cover-pixel and adopting modulus operation based on 2k. The experimental results show that the proposed method has better performance than methods compared and the highest hiding capacity can reach 4 bits per pixel and the average PSNR of stego-images is 34.83 dB. The source code and related materials are made to public to make it easy for researchers to verify the work and stimulate further research.
Annotation Cost Reduction of Stream-based Active Learning by Automated Weak Labeling using a Robot Arm

Stream-based active learning (AL) is an efficient training data collection method, and it is used to reduce human annotation cost required in machine learning. However, it is difficult to say that the human cost is low enough because most previous studies have assumed that an oracle is a human with domain knowledge. In this study, we propose a method to replace a part of the oracle’s work in stream-based AL by self-training with weak labeling using a robot arm. A camera attached to a robot arm takes a series of image data related to a streamed object, which should have the same label. We use this information as a weak label to connect a pseudo-label (estimated class label) and a target instance. Our method selects two data from a series of image data; high confidence data for correcting pseudo-labels and low confidence data for improving the performance of the classifier. We paired a pseudo-label provided to high confidence data with a target instance (low confidence data). By using this technique, we mitigate the inefficiency in self-training, that is, difficulty in creating pseudo-labeled training data with a high impact on the target classifier. In the experiments, we employed the proposed method in the classification task of objects on a belt conveyor. We evaluated the performance against human cost on multiple scenarios considering the temporal variation of data. The proposed method achieves the same or better performance as the conventional methods while reducing human cost.
Classical Chinese Poetry Generation based on Transformer-XL

Classical Chinese poetry is a kind of formatted text with phonological patterns. It has been a big challenge for the classical Chinese poetry generation. Because deep learning technology has made great progress in image and text processing, we present a classical Chinese poetry generation model based on Transformer-XL that can capture longer-term dependency than the original transformer. Our model creates the first character according to the title specified by a user and then concatenates the generated character as the input to predict the next character until reaches the predefined number of characters. Experimental results show that our model can generate poems that have good format, consistency and rhythm.
Discrete-to-deep reinforcement learning methods

Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. In complex problems, a neural RL approach is often able to learn a better solution than tabular RL, but generally takes longer. This paper proposes two methods, Discrete-to-Deep Supervised Policy Learning (D2D-SPL) and Discrete-to-Deep Supervised Q-value Learning (D2D-SQL), whose objective is to acquire the generalisability of a neural network at a cost nearer to that of a tabular method. Both methods combine RL and supervised learning (SL) and are based on the idea that a fast-learning tabular method can generate off-policy data to accelerate learning in neural RL. D2D-SPL uses the data to train a classifier which is then used as a controller for the RL problem. D2D-SQL uses the data to initialise a neural network which is then allowed to continue learning using another RL method. We demonstrate the viability of our algorithms with Cartpole, Lunar Lander and an aircraft manoeuvring problem, three continuous-space environments with low-dimensional state variables. Both methods learn at least 38% faster than baseline methods and yield policies that outperform them.
ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment

With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of realistic text on chips datasets to act as a strong foundation. Hence, a text on chips dataset, ICText is used as the main target for the proposed Robust Reading Challenge on Integrated Circuit Text Spotting and Aesthetic Assessment (RRC-ICText) 2021 to encourage the research on this problem. Throughout the entire competition, we have received a total of 233 submissions from 10 unique teams/individuals. Details of the competition and submission results are presented in this report.
Exploring the Role of Local and Global Explanations in Recommender Systems

Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining individual recommendations, or global, explaining the recommender model overall. Despite their widespread use, there has been little investigation into the relative benefits of the two explanation approaches. We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender to analyze how providing local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiently identifying false positive and negative recommendations. However, we note that the two explanation approaches may be better compared in a higher-stakes or more opaque domain.
Instructional Guidance in Extended Reality for Learning

Experiential learning is the process of learning through experience or more specifically “learning through reflection on doing”. These experiences can be simulated in Extended Reality (XR) systems– interactive environments generated by computer technology combining real and virtual worlds. Such systems have shown to be excellent tools for providing guidance to teach different skills and activities. In this research, we introduce a comprehensive design space grounded in (i) XR technologies, and (ii) theoretical underpinnings of experiential learning and guidance. We use this design space to describe key design decisions in prior work and elucidate existing gaps. Employing the design space, we intend to build an adaptive, personalised instructional guidance system in XR, which offers various levels of guidance that adapt to users’ learning progress. We utilise the Kolb’s experiential learning cycle considering all four stages and transitions within it. With XR technologies, we offer the environment that can provide guidance to support learners to move spontaneously from one stage to another in the experiential learning cycle and further progress on the learning spiral. It is the latter that we explore in this research by adopting potential learning scenarios such as language learning and spatial navigation. We attempt to investigate how different factors of instructional guidance, such as the amount (minimal guidance, full guidance, and adaptive guidance) and the type (from direct instructions to associative models based examples), would affect the experiential learning process and learning outcomes of different learning scenarios in XR environments.
Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.