Reviewerly/exHarmony huggingface dataset:
Architecture and Proof of Concept Design for Community Marketplace Using Blockchain Technology and Fuzzy Intelligence

In a traditional marketplace, a centralized system determines the price of each commodity. However, here is the caveat, this system can introduce monopoly and face a single point of failure. In a decentralized marketplace, members can decide the pricing based on consensus by considering the quality, delivery time, or the current demand for the said product in the market. Blockchain technology is a secure, decentralized, and distributed ledger used to manage transactions. The immutable ledger stores the encrypted transaction details. This paper proposes an architecture for a blockchain-enabled community marketplace featuring dynamic pricing. This paper also discusses the blockchain-powered kiosk platform embedded with sensors to identify the quality and quantity of the commodities. A fuzzy logic-based smart contract is developed to monitor these parameters and decide on the pricing of the products sold through the kiosk. A customer can view the price in real-time through a mobile application or the kiosk's user interface. The system processes a transaction only after the customer accepts the proposed price. The IOTA decentralized distributed ledger platform will retain the customer information, sensor data, dynamic pricing from the fuzzy logic system, and the transaction details. This paper discusses the following: system architecture of the marketplace platform, the network configuration of the test network, fuzzy logic-based dynamic pricing algorithms, and the mobile application developed for end-users to perform the transactions on the platform.
Experimental optical encryption based on random mask encoding and deep learning

We present an experimental scheme for optical encryption using random mask encoding and deep learning technique. A phase image is encrypted into a speckle pattern by a random amplitude modulation in the optical transmission. Before decryption processing, a neural network model is used to learn the mapping relationship between the pure-phase object and the speckle image rather than characterizing the filter film used in the scheme explicitly or parametrically. The random binary mask is made by a polyethylene terephthalate film and 2500 object-speckle pairs are used for training. The experimental results demonstrate that the proposed scheme based on deep learning could be successfully used as a random binary mask encrypted image processor, which can quickly output the primary image with high quality from the cyphertext.
Machine Learning Approach for Identifying Survival of Bone Marrow Transplant Patients

Bone marrow transplant is also known as hematopoietic stem cell transplant which replaces unhealthy cells or marrow with healthy cells. This is useful for curing many diseases especially few types of cancer. The success rate of bone marrow transplant depends on many factors like genetic matching with donors and also on some of the features of recipients. In this paper, we have implemented different machine learning approaches like logistic regression, multilayer perceptron, random forest and Bayes network to find out the survival of bone marrow transplant patients. The same techniques have been applied on full dataset as well as on reduced dataset. The dimensionality of the dataset is reduced using correlation feature subset method. The results show that reduced dataset provides better accuracy with all the implemented methods. Multilayer perceptron and Bayes network outshined other methods with 94.1% accuracy.
Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service

Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.
Global Color Consistency Correction for Large-Scale Images in 3-D Reconstruction

Global color consistency correction for multiview images in three-dimensional (3-D) reconstruction is an important problem. The color differences between the images will affect the result of dense matching, thereby reducing the geometric accuracy of the mesh model. Moreover, it will also affect the result of texture mapping, causing color differences in the textured model. The color correction method based on global optimization is mainly used to solve this problem. And existing methods usually use sparse matching points as the color correspondences, but the correction results are not accurate enough as a result of the sparsity of the points. Besides, their efficiency of solving large-scale images globally is low. This article proposes a novel color correction method to eliminate the color differences between large-scale multiview images effectively. The core idea of our method is to group images by graph partition algorithm, and then perform intragroup correction and intergroup correction in sequence. First, for each pair of images, we calculate the reliable matching regions around the sparse points as the color correspondences according to the local homography principle. Compared with sparse matching points, our strategy can achieve more accurate color correction results. Next, for large-scale images, we partition them into many groups. For each group of images, the correction parameters are solved to eliminate the color differences of the images included in the group. Finally, we eliminate the color differences between groups by intergroup correction to achieve overall color consistency. Experimental results on typical datasets demonstrate that the proposed method is better than the current representative methods. The proposed method shows better color consistency in the extreme cases, and also exhibits higher computational efficiency on large-scale image sets.
Cross-Lingual Automatic Short Answer Grading

Massive open online courses and other online study opportunities are providing easier access to education for more and more people around the world. However, one big challenge is still the language barrier: Most courses are available in English, but only 16% of the world’s population speaks English [1]. The language challenge is especially evident in written exams, which are usually not provided in the student’s native language. To overcome these inequities, we analyze AI-driven cross-lingual automatic short answer grading. Our system is based on a Multilingual Bidirectional Encoder Representations from Transformers model [2] and is able to fairly score free-text answers in 26 languages in a fully-automatic way with the potential to be extended to 104 languages. Augmenting training data with machine translated task-specific data for fine-tuning even improves performance. Our results are a first step to allow more international students to participate fairly in education.
How we explore, interpret, and solve complex problems: A cross-national study of problem-solving processes

Complex problem solving (CPS) is considered an important educational outcome in the 21st century. Despite its importance, we have only little only knowledge of its measurability, development, or comparability in some countries, in particular in those with a short history of computer-based assessment. The results of the current study provide insights into the validity of CPS measurements and shed light on the different behavioral patterns and test-taking behavior in two convenience samples with different sample characteristics of Jordanian (N = 431) and Hungarian (N = 1844) students as they solve complex problems. CPS proved to be measurement-invariant in Jordan and Hungary among university students. Analyzing log data, we identified differences in students' test-taking behavior in terms of the effectiveness of their exploration strategy, time-on-task, and number of trials. Based on the students' exploration strategy behavior, we identified four latent classes in both samples. The tested process indicators proved to be non-invariant over the different latent profiles; that is, there are differences in the role of the number of manipulations executed, time-on-task, and type of strategy used in actual problem-solving achievement between students that fall within different profiles. This study contributes to our understanding of how students from different educational contexts behave while solving complex problems.
Learning to Walk with Dual Agents for Knowledge Graph Reasoning

Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths. However, existing multi-hop reasoning approaches only work well on short reasoning paths and tend to miss the target entity with the increasing path length. This is undesirable for many reasoning tasks in real-world scenarios, where short paths connecting the source and target entities are not available in incomplete KGs, and thus the reasoning performances drop drastically unless the agent is able to seek out more clues from longer paths. To address the above challenge, in this paper, we propose a dual-agent reinforcement learning framework, which trains two agents (Giant and Dwarf) to walk over a KG jointly and search for the answer collaboratively. Our approach tackles the reasoning challenge in long paths by assigning one of the agents (Giant) searching on cluster-level paths quickly and providing stage-wise hints for another agent (Dwarf). Finally, experimental results on several KG reasoning benchmarks show that our approach can search answers more accurately and efficiently, and outperforms existing RL-based methods for long path queries by a large margin.
Absolute phase retrieval of shiny objects using fringe projection and deep learning with computer-graphics-based images

Fringe projection profilometry is a high-precision method used to measure the 3D shape of an object by projecting sinusoidal fringes onto an object. However, fringes projected onto a metallic or shiny object are distorted nonlinearly, which causes significant measurement errors. A high-precision measurement method for shiny objects that employs computer graphics (CG) and deep learning is proposed. We trained a deep neural network by projecting fringes on a shiny object in CG space. Our results show that the method can reduce the nonlinear fringe distortion caused by gloss in real space.
Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver

The recognition of entanglement states is a notoriously difficult problem when no prior information is available. Here, we propose an efficient quantum adversarial bipartite entanglement detection scheme to address this issue. Our proposal reformulates the bipartite entanglement detection as a two-player zero-sum game completed by parameterized quantum circuits, where a two-outcome measurement can be used to query a classical binary result about whether the input state is bipartite entangled or not. In principle, for an N-qubit quantum state, the runtime complexity of our proposal is O(poly(N)T) with T being the number of iterations. We experimentally implement our protocol on a linear optical network and exhibit its effectiveness to accomplish the bipartite entanglement detection for 5-qubit quantum pure states and 2-qubit quantum mixed states. Our work paves the way for using near-term quantum machines to tackle entanglement detection on multipartite entangled quantum systems.
MetaCVR

Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctuation (DDF) issue. DDF prevents existing CVR models from being effective since 1) several months of data are needed to train CVR models sufficiently in small scenarios, leading to considerable distribution discrepancy between training and online serving; and 2) e-commerce promotions have significant impacts on small scenarios, leading to distribution uncertainty of the upcoming time period. In this work, we propose a novel CVR method named MetaCVR from a perspective of meta learning to address the DDF issue. Firstly, a base CVR model which consists of a Feature Representation Network (FRN) and output layers is designed and trained sufficiently with samples across months. Then we treat time periods with different data distributions as different occasions and obtain positive and negative prototypes for each occasion using the corresponding samples and the pre-trained FRN. Subsequently, a Distance Metric Network (DMN) is devised to calculate the distance metrics between each sample and all prototypes to facilitate mitigating the distribution uncertainty. At last, we develop an Ensemble Prediction Network (EPN) which incorporates the output of FRN and DMN to make the final CVR prediction. In this stage, we freeze the FRN and train the DMN and EPN with samples from recent time period, therefore effectively easing the distribution discrepancy. To the best of our knowledge, this is the first study of CVR prediction targeting the DDF issue in small-scale recommendation scenarios. Experimental results on real-world datasets validate the superiority of our MetaCVR and online A/B test also shows our model achieves impressive gains of 11.92% on PCVR and 8.64% on GMV.
Pasteables: A Flexible and Smart "Stick-and-Peel'' Wearable Platform for Fitness &amp; Athletics

Wearable technologies are becoming increasingly popular and transforming our fitness and sports industry. Monitoring health parameters and body activity can help achieve fitness goals, improve sports performance, and aid in physiotherapy. However, state-of-the-art wearable devices are often not flexible, expensive, hard to set up, or have privacy concerns. They are designed and optimized for a specific application with tight hardware-software integration and it is hard or impossible to re-purpose them for diverse use cases. In this paper, we propose a flexible, re-configurable human body movement and health monitoring platform, called "Pasteables". It is a stick-and-peel device (which acts like a band-aid) that attaches to the skin or clothing and can create an on-body network of smart wearables. Given an application, Pasteables can be easily adapted to its requirements while ensuring good performance and privacy. We present the overall architecture, system design steps, and the configuration process. We have designed a set of functional prototypes and configured them for performing a variety of camera-less pose and posture detection tasks, which are important for athletes, professional dancing, physiotherapy, and fitness workouts. We note that the Pasteables can perform pose/posture detection without external stationary reference at low cost and high accuracy.
Batching Base Oblivious Transfers

Protocols that make use of oblivious transfer (OT) rarely require just one instance. Usually, a batch of OTs is required—notably, when generating base OTs for OT extension. There is a natural way to optimize 2-round OT protocols when generating a batch, by reusing certain protocol messages across all instances. In this work we show that this batch optimization is error prone. We catalog many implementations and papers that have an incorrect treatment of this batch optimization, some of them leading to catastrophic leakage in OT extension protocols. We provide a full treatment of how to properly optimize recent 2-round OT protocols for the batch setting. Along the way we show several performance improvements to the OT protocol of McQuoid, Rosulek, and Roy (ACM CCS 2020). In particular, we show an extremely simple OT construction that may be of pedagogical interest.
Review on blockchain technology and its application to the simple analysis of intellectual property protection

Blockchain is a widely used decentralised infrastructure. Blockchain technology has the decentration of network, the unforgeability of block data, etc. Therefore, blockchain technology has developed rapidly in recent years, and many organisations are involved. Applications are generally optimistic. This paper systematically introduces the background development status of blockchain, and analyses the operation mechanism, characteristics and possible application scenarios of blockchain technology from a technical perspective. Finally, the blockchain technology is applied to the intellectual property protection method as an example to study domestic and foreign examples and analyse existing problems. The review article aims to provide assistance for the application of blockchain technology.
Comparison of Remote Subjective Assessment Strategies in the Context of the JPEG Pleno Point Cloud Activity

In this work we compare two different options to perform on-line subjective quality assessment experiments in the context of the Call for Evidence on JPEG Pleno Point Cloud Coding. A deep-learning based point cloud codec submitted to the Call was tested against current MPEG point cloud compression methods. The first option is based on participants downloading the entire set of stimuli and running a set of scripts in MATLAB to perform the experiment. The second option involves the participants accessing a server on the web and viewing and judging the stimuli using a web browser. Quality scores compiled using both methods were compared showing strong correlation. A second analysis compared the quality scores with those obtained in a prior laboratory-based study using higher resolution screens. The entire study also brought to light each option’s unique advantages and disadvantages that make each one better suited to specific types of subjective evaluation contexts and situations.
An intelligent evaluation model of bilingual teaching quality based on network resource sharing

Aiming at the poor application performance of traditional model for bilingual teaching quality evaluation, this paper proposes a model construction method of bilingual teaching quality evaluation based on network resource sharing. The evaluation indicators are selected reasonably, and an intelligent evaluation model of bilingual teaching quality based on network resource sharing is constructed. The evaluation indicators in the model are compared and weighted by analytic hierarchy process (AHP), and a fuzzy comprehensive evaluation matrix is established. The bilingual teaching quality evaluation has been realised. The experimental results show that the fluctuation range of evaluation error is between 1.6 and 1.7, and the evaluation error is small. Through the questionnaire survey, it is found that the time for teachers to compile teaching plans is shortened by 0.4 hours, the students' scores are improved by 1.5 points, the quality of bilingual teaching is improved, and the satisfaction of teachers and students is improved.
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.
Graph-powered learning for social networks

Recently, deep learning technology has achieved remarkable progress in compressed video quality enhancement. However, the existing methods fail to consider the fact that the regions with different compression losses contain varied effective information. To address this issue, this paper proposes a Compression Loss-based Spatial-Temporal Attention (CLSTA) module that predicts the compression loss-based attention of each pixel when restoring target frames. Based on this, the pixels with lower compression losses contribute more to restoring target frames. Meanwhile, the compression-domain information in the compressed video stream is exploited to overcome the difficulty in inferring compression loss-based attention directly from the pixel-domain. With a slight modification, the CLSTA module can be easily integrated into the existing methods for end-to-end training. A series of experiments have been conducted in this study to validate the effectiveness and generality of the CLSTA module for compressed video quality enhancement. By adding the CLSTA module to the state-of-the-art method STDF, the proposed method achieves an average PSNR improvement of 0.96 dB and a BD-rate reduction of 26.85% under the Low-Delay P configuration, which is respectively 0.06 dB and 1.83% better than the baseline STDF. In the best case, the proposed method obtains 0.19 dB higher PSNR than STDF for a single frame.
A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer

Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.