Reviewerly/exHarmony huggingface dataset:
An integrated photonics engine for unsupervised correlation detection

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge 2 Sb 2 Te 5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.
A Novel Decentralized E-Commerce Transaction System Based on Blockchain

With the rapid development of e-commerce systems, the centralized service model gradually fails to meet the needs of SMEs. In the existing centralized e-commerce system, users’ transaction data and reputation scores are stored in a centralized cloud server, which has high storage cost, low processing efficiency, and the data is vulnerable to attacks and leaks. However, the existing decentralized e-commerce systems more than its reputation system to store the average credit score evaluation are receiving unfair evaluations against risk. The system of malicious nodes and no disciplinary measures, is not conducive to system development. To solve this problem, this paper proposes a blockchain-based decentralized e-commerce transaction system. The system commodity information is stored in the Interplanetary File System (IPFS) and the returned commodity addresses are stored in the blockchain to enhance the service performance. This paper proposes a reputation evaluation model based on multi-criteria decision making (MCDM), which can effectively resist unfair evaluation and collusion attacks, and proposes an incentive mechanism based on reputation value to reward and punish nodes, thus promoting the good circulation of the system. We implement the proposed system based on Ethereum. The experimental results show that the system has a small communication cost, accurately reflects the user’s reputation value, and has good availability and reliability.
MMNet: A Model-based Multimodal Network for Human Action Recognition in RGB-D Videos

Human action recognition (HAR) in RGB-D videos has been widely investigated since the release of affordable depth sensors. Currently, unimodal approaches (e.g., skeleton-based and RGB video-based) have realized substantial improvements with increasingly larger datasets. However, multimodal methods specifically with model-level fusion have seldom been investigated. In this paper, we propose a model-based multimodal network (MMNet) that fuses skeleton and RGB modalities via a model-based approach. The objective of our method is to improve ensemble recognition accuracy by effectively applying mutually complementary information from different data modalities. For the model-based fusion scheme, we use a spatiotemporal graph convolution network for the skeleton modality to learn attention weights that will be transferred to the network of the RGB modality. Extensive experiments are conducted on five benchmark datasets: NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, Northwestern-UCLA Multiview, and Toyota Smarthome. Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR. We also tested our MMNet on an RGB video dataset Kinetics 400 that contains more outdoor actions, which shows consistent results with those of RGB-D video datasets.
Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis

Bitcoin (BTC) prices are fluctuating continuously to the extremes. The Bitcoin market witnessed a crash during the second quarter of 2021 that was purely guided by the investors’ sentiments. Are the Bitcoin prices influenced only by market sentiments or do any factors influence them? In this paper, we applied a triangulation approach; mixed-methods research was used in which a qualitative study was complemented by a quantitative method. Both the qualitative and quantitative data of time periods 2016–2021 were examined to find whether the Bitcoin market prices are influenced by market sentiments. For analysing market sentiments, the posts and sentiments from 2016 to 2021 of an internet forum “Bitcointalk” were used. For strengthening the findings of qualitative analysis, we used quantitative data of the BTC market. We also used search data from Google Trends for providing further insights. Our research shows a crossmatch between quantitative trends on Bitcoin market prices and qualitative matrix of sentiments. We have also observed an artificial investment intention in the form of digital nudges playing the field of the Bitcoin market to boost investment.
Velocity Estimation and Cost Map Generation for Dynamic Obstacle Avoidance of ROS Based AMR

In the past few years, due to the growth of the open-source community and the popularity of perceptual computing resources, the ROS (Robotic Operating System)Ecosystem has been widely shared and used in academia, industrial applications, and service fields. With the advantages of reusability of algorithms and system modularity, service robot applications are flourishing via the released ROS navigation framework. In the ROS navigation framework, the grid cost maps are majorly designed for path planning and obstacle avoidance with range sensors. However, the robot will often collide with dynamic obstacles since the velocity information is not considered within the navigation framework in time. This study aims to improve the feasibility of high-speed dynamic obstacle avoidance for an ROS-based mobile robot. In order to enable the robot to detect and estimate dynamic obstacles from a first-person perspective, vision tracking and a laser ranger with an Extend Kalman Filter (EKF) have been applied. In addition, an innovative velocity obstacle layer with truncated distance is implemented for the path planner to analyze the performances between the simulated and actual avoidance behavior. Finally, via the velocity obstacle layer, as the robot faces the high-speed obstacle, safe navigation can be achieved.
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder

Recent advances in deep learning have led to superhuman performance across a variety of applications. Recently, these methods have been successfully employed to improve the rate-distortion performance in the task of image compression. However, current methods either use additional post-processing blocks on the decoder end to improve compression or propose an end-to-end compression scheme based on heuris-tics. For the majority of these, the trained deep neural networks (DNNs) are not compatible with standard encoders and would be difficult to deploy on personal com-puters and cellphones. In light of this, we propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends, an approach we call Neural JPEG. We propose frequency domain pre-editing and post-editing methods to optimize the distribution of the DCT coefficients at both encoder and decoder ends in order to improve the stan-dard compression (JPEG) method. Moreover, we design and integrate a scheme for jointly learning quantization tables within this hybrid neural compression framework. In summary, our contributions are as follows:
Stereotactic co-axial projection imaging for augmented reality neuronavigation: a proof-of-concept study

Background: Lack of intuitiveness and poor hand-eye coordination present a major technical challenge in neurosurgical navigation. Methods:We developed an integrated dexterous stereotactic co-axial projection imaging (sCPI) system featuring orthotopic image projection for augmented reality (AR) neurosurgical navigation.The performance characteristics of the sCPI system, including projection resolution and navigation accuracy, were quantitatively verified.The resolution of the sCPI was tested with a USAF1951 resolution test chart.The stereotactic navigation accuracy of the sCPI was measured using a calibration panel with a 7×7 circle array pattern.In benchtop validation, the navigation accuracy of the sCPI and the BrainLab Kick Navigation Station was compared using a skull phantom with 8 intracranial targets.Finally, we demonstrated the potential clinical application of sCPI through a clinical trial. Results:The resolution test showed that the resolution of the sCPI was 1.3 mm.In a stereotactic navigation accuracy test, the maximum and minimum error of the sCPI was 2.9 and 0.3 mm, and the mean error was 1.5 mm.The stereotactic navigation accuracy test also showed that the navigation error of the sCPI would increase with the pitch and yaw angle, but there was no obvious difference in navigation errors caused by different yaw directions, which meant that the navigation error is unbiased across all directions.The benchtop validation showed that the average navigation errors for the sCPI system and the Kick Navigation Station were 1.4±0.8 and 1.8±0.7 mm, the medians were 1.3 and 1.9 mm, and the average preparation times were 3 min 24 sec and 6 min 8 sec, respectively.The clinical feasibility of sCPI-assisted neurosurgical navigation was demonstrated in a clinical study.In comparison with the BrainLab device, the sCPI system required less time for preoperative preparation and enhanced the clinician experience in intraoperative visualization and navigation. Conclusions:The sCPI technique can be potentially used in many surgical applications for intuitive visualization of medical information and intraoperative guidance of surgical trajectories.
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System

As a more natural and intelligent interaction manner, multimodal task-oriented dialog system recently has received great attention and many remarkable progresses have been achieved.Nevertheless, almost all existing studies follow the pipeline to first learn intra-modal features separately and then conduct simple feature concatenation or attention-based feature fusion to generate responses, which hampers them from learning inter-modal interactions and conducting crossmodal feature alignment for generating more intention-aware responses.To address these issues, we propose UniTranSeR, a Unified Transformer Semantic Representation framework with feature alignment and intention reasoning for multimodal dialog systems.Specifically, we first embed the multimodal features into a unified Transformer semantic space to prompt inter-modal interactions, and then devise a feature alignment and intention reasoning (FAIR) layer to perform cross-modal entity alignment and fine-grained key-value reasoning, so as to effectively identify user's intention for generating more accurate responses.Experimental results verify the effectiveness of UniTranSeR, showing that it significantly outperforms state-of-the-art approaches on the representative MMD dataset.
PipeNTT: A Pipelined Number Theoretic Transform Architecture

Polynomial multiplication is the key and time-consuming operation among various operators in Post-Quantum Cryptography (PQC), which aims to find quantum-resistant algorithms to prevent attacks launched by quantum computers. Number Theoretic Transform (NTT) is an efficient algorithm that can accelerate the polynomial multiplication from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(nlog(n))$ </tex-math></inline-formula> . In this brief, we present a pipelined NTT (PipeNTT) hardware architecture in FPGA to achieve high throughput with fewer hardware resources. The dataflow and the butterfly unit are optimized to minimize the latency. To fulfil the proposed dataflow, a Block RAM (BRAM) based reordering unit is designed to further reduce the hardware resource. Moreover, our architecture can also be applied to Inverse-NTT (INTT). Compared to state-of-the-art parallel designs, our design achieves a 30% lower area-time product with 3x less memory space requirement.
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition

Cross-lingual named entity recognition task is one of the critical problems for evaluating the potential transfer learning techniques on low resource languages.Knowledge distillation using pre-trained multilingual language models between source and target languages have shown their superiority in transfer.However, existing cross-lingual distillation models merely consider the potential transferability between two identical single tasks across both domains.Other possible auxiliary tasks to improve the learning performance have not been fully investigated.In this study, based on the knowledge distillation framework and multitask learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain.Specifically, an entity recognizer and a similarity evaluator are first trained in parallel as two teachers from the source domain.Then, two tasks in the student model are supervised by these teachers simultaneously.Empirical studies on the three datasets across 7 different languages confirm the effectiveness of the proposed model.
GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task.
Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance

Locating a missing child or elderly person in a large gathering through face recognition in videos is still challenging because of various dynamic factors. In this paper, we present an intelligent mechanism for tracking missing persons in an unconstrained large gathering scenario of Al-Nabawi Mosque, Madinah, KSA. The proposed mechanism in this paper is unique in two aspects. First, there are various proposals existing in the literature that deal with face detection and recognition in high-quality images of a large crowd but none of them tested tracking of a missing person in low resolution images of a large gathering scenario. Secondly, our proposed mechanism is unique in the sense that it employs four phases: (a) report missing person online through web and mobile app based on spatio-temporal features; (b) geo fence set estimation for reducing search space; (c) face detection using the fusion of Viola Jones cascades LBP, CART, and HAAR to optimize the results of the localization of face regions; and (d) face recognition to find a missing person based on the profile image of reported missing person. The overall results of our proposed intelligent tracking mechanism suggest good performance when tested on a challenging dataset of 2208 low resolution images of large crowd gathering.
CERT: Continual Pre-training on Sketches for Library-oriented Code Generation

Code generation is a longstanding challenge, aiming to generate a code snippet based on a natural language description. Usually, expensive text-code paired data is essential for training a code generation model. Recently, thanks to the success of pre-training techniques, large language models are trained on large unlabelled code corpora and perform well in generating code. In this paper, we investigate how to leverage an unlabelled code corpus to train a model for library-oriented code generation. Since it is a common practice for programmers to reuse third-party libraries, in which case the text-code paired data are harder to obtain due to the huge number of libraries. We observe that library-oriented code snippets are more likely to share similar code sketches. Hence, we present CERT with two steps: a sketcher generates the sketch, then a generator fills the details in the sketch. Both the sketcher and generator are continually pre-trained upon a base model using unlabelled data. Also, we carefully craft two benchmarks to evaluate library-oriented code generation named PandasEval and NumpyEval. Experimental results have shown the impressive performance of CERT. For example, it surpasses the base model by an absolute 15.67% improvement in terms of pass@1 on PandasEval. Our work is available at https://github.com/microsoft/PyCodeGPT.
A Quantum Planner for Robot Motion

The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot’s behavior. According to the production rules, the planning of the robot activities is processed in a recognize–act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up.
IoT Wheelchair Control System Based on Multi-Mode Sensing and Human-Machine Interaction

Traditional wheelchairs are unable to actively sense the external environment during use and have a single control method. Therefore, this paper develops an intelligent IoT wheelchair with the three functions, as follows. (1) Occupant-wheelchair-environment multimode sensing: the PAJ7620 sensor is used to recognize gesture information, while GPS (Global Positioning System) and IMU (Inertial Measurement Unit) sensors are used to sense positioning, speed and postural information. In addition, Lidar, DHT11, and BH1750 sensors obtain environmental information such as road information, temperature and humidity and light intensity. (2) Fusion control scheme: a mobile control scheme based on rocker and gesture recognition, as well as a backrest and footrest lifting, lowering and movement control scheme based on Tencent Cloud and mobile APP (Application). (3) Human-machine interaction: the wheelchair is docked to Tencent IoT Explorer through ESP8266 WiFi module, using MQTT (Message Queuing Telemetry Transport) protocol is used to upload sensory data, while the wheelchair status can be viewed and controlled on the APP. The wheelchair designed in this paper can sense and report the status of the occupant, environment and wheelchair in real time, while the user can view the sensory data on the mobile APP and control the wheelchair using the rocker, gestures and APP.
Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets

There are growing concerns about the mortality due to Breast cancer many of which often result from delayed detection and treatment. So an effective computational approach is needed to develop a predictive model which will help patients and physicians to manage the situation timely. This study presented a Weighted Bayesian Belief Network (WBBN) modeling for breast cancer prediction using the UCI breast cancer dataset. New automated ranking method was used to assign proper weights to attribute value pair based on their impact on causing the disease. Association between attributes was generated using weighted association rule mining between two attributes, multiattributes, and with class labels to generate rules. Weighted Bayesian confidence and weighted Bayesian lift measures were used to produce strong rules to build the model. To build WBBN, the Open Markov tool was used for structure and parametric learning using generated strong rules. The model was trained using 70% records and tested on 30% records with a threshold value of minimum support = 36% and confidence = 70% which produced results with an accuracy of 97.18%. Experimental results show that WBBN achieved better results in most cases compared to other predictive models. The study would contribute to the fight against breast cancer and the quality of treatment.
Text Generation with Text-Editing Models

Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts. 2022.
Long Context Question Answering via Supervised Contrastive Learning

Long-context question answering (QA) tasks require reasoning over a long document or multiple documents.Addressing these tasks often benefits from identifying a set of evidence spans (e.g., sentences), which provide supporting evidence for answering the question.In this work, we propose a novel method for equipping long-context QA models with an additional sequence-level objective for better identification of the supporting evidence.We achieve this via an additional contrastive supervision signal in finetuning, where the model is encouraged to explicitly discriminate supporting evidence sentences from negative ones by maximizing question-evidence similarity.The proposed additional loss exhibits consistent improvements on three different strong longcontext transformer models, across two challenging question answering benchmarks -Hot-potQA and QAsper.