Reviewerly/exHarmony huggingface dataset:
Accelerating Deep Learning Inference in Constrained Embedded Devices Using Hardware Loops and a Dot Product Unit

Deep learning algorithms have seen success in a wide variety of applications, such as machine translation, image and speech recognition, and self-driving cars. However, these algorithms have only recently gained a foothold in the embedded systems domain. Most embedded systems are based on cheap microcontrollers with limited memory capacity, and, thus, are typically seen as not capable of running deep learning algorithms. Nevertheless, we consider that advancements in compression of neural networks and neural network architecture, coupled with an optimized instruction set architecture, could make microcontroller-grade processors suitable for specific low-intensity deep learning applications. We propose a simple instruction set extension with two main components-hardware loops and dot product instructions. To evaluate the effectiveness of the extension, we developed optimized assembly functions for the fully connected and convolutional neural network layers. When using the extensions and the optimized assembly functions, we achieve an average clock cycle count decrease of 73% for a small scale convolutional neural network. On a per layer base, our optimizations decrease the clock cycle count for fully connected layers and convolutional layers by 72% and 78%, respectively. The average energy consumption per inference decreases by 73%. We have shown that adding just hardware loops and dot product instructions has a significant positive effect on processor efficiency in computing neural network functions.
Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities

Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals. Nevertheless, understanding user utterances with high accuracy remains a challenging task with these frameworks. Particularly, when building chatbots with large volume of domain-specific entities. In this paper, we describe the challenges and lessons learned from building a large scale virtual assistant for understanding and responding to equipment-related complaints. In the process, we describe an alternative scalable framework for: 1) extracting the knowledge about equipment components and their associated problem entities from short texts, and 2) learning to identify such entities in user utterances. We show through evaluation on a real dataset that the proposed framework, compared to off-the-shelf popular ones, scales better with large volume of entities being up to 30% more accurate, and is more effective in understanding user utterances with domain-specific entities.
Unsupervised anomaly detection of the gas turbine operation via convolutional auto-encoder

This paper proposes a combination of convolutional neural network and auto-encoder (CAE) for unsupervised anomaly detection of industrial gas turbines. Autonomous monitoring systems protect the gas turbines, with the settings unchanged in their lifetime. Those systems can not detect any abnormal operation patterns which potentially risk the equipment after long-term exposure. Recently, machine learning and deep learning models are applied for industries to detect those anomalies under the nominal working range. However, for gas turbine protection, not much deep learning (DL) models are introduced. The proposed CAE detects irregular signals in unsupervised learning by combining a convolutional neural network (CNN) and auto-encoder (AE). CNN exponentially reduces the computational cost and decrease the amount of training data, by its extraction capabilities of essential features in spatial input data. A CAE identifies the anomalies by adapting characteristics of an AE, which identifies any errors larger than usual pre-trained, reconstructed errors. Using the Keras library, we developed an AE structure in one-dimensional convolution layer networks. We used actual plant operation data set for performance evaluation with conventional machine learning (ML) models. Compared to the isolation forest (iforest), k-means clustering (k-means), and one-class support vector machine (OCSVM), our model accurately predicts unusual signal patterns identified in the actual operation than conventional ML models.
Cost-sensitive metaheuristic technique for credit card fraud detection

To prevent online financial losses, credit card frauds need to be addressed and it should be controlled. To minimize the credit card frauds, fraudulent credit card transactions should be identified and minimized. Keeping this fact in view, in this paper, a cost-sensitive metaheuristic technique (CSFPA: cost-sensitive learning flower pollination metaheuristic algorithm) is proposed to minimize the misclassification cost of credit card transactions from class imbalance data using the correlation-based feature section method (CFS), flower pollination algorithm (FPA), and cost-sensitive classifier. To identify the fraudulent transactions, the random forest (RF) ensemble classifier is used as a base learner in the cost-sensitive classifier for classification tasks and the proposed CSFPA technique has been tested on the Brazilian bank dataset. The performance of proposed CSFPA has been compared with cost insensitive techniques and cost-sensitive techniques such as NB (Naive Base), KNN (K-Nearest Neighbors), LB (Logit Boost), CSForest (Cost-sensitive decision forest), NBBFS (NB Best First Search) KNNGSS (KNN Greedy Stepwise Search), and CSFCS (CSForest Cuckoo Search). The experimental outcomes revealed that the proposed CSFPA technique has obtained encouraging outcomes for handling misclassification costs (Total cost and Avg. cost) and also has outperformed (FPR, Precision and Recall) compared to all the other approaches.
De-Wipimization: Detection of data wiping traces for investigating NTFS file system

Data wiping is used to securely delete securely unwanted files. However, the misuse of data wiping can destroy pieces of evidence to be spoiled in a digital forensic investigation. To cope with the misuse of data wiping, we proposed an anti-anti-forensic method based on NTFS transaction features and a machine learning algorithm. This method allows investigators to obtain information regarding ‘which files are wiped’ and ‘which data wiping tools and data sanitization standards used’. In this study, we achieved good identification of data wiping traces in the NTFS file system. Leveraging the efficiency of machine learning models, our method effectively recognizes wiped partitions and files in the NTFS file system and identifies tools used in data sanitization.
Nonparametric maximum likelihood estimation using neural networks

Estimation of probability density functions is an essential component of various applications. Nonparametric techniques have been widely used for this task owing to the difficulty in parameterization of data. In particular, certain kernel density estimation methods have been developed. However, they are either incapable of maximum likelihood estimation or require the maintenance of a training set to process new patterns. In this study, a new approach, called the nonparametric maximum likelihood neural network (MLNN), is proposed. This is a nonparametric method, relying on maximum likelihood and neural network. It is compact in form and does not require the maintenance of training patterns. Theoretical and experimental analyses demonstrate the efficacy of the proposed approach.
Proprioceptive drift is affected by the intermanual distance rather than the distance from the body’s midline in the rubber hand illusion

Abstract In the rubber hand illusion (RHI), simultaneous brush stroking of a subject’s hidden hand and a visible rubber hand induces a transient illusion of the latter to “feel like it’s my hand” and a proprioceptive drift of the hidden own hand toward the rubber hand. Recent accounts of the RHI have suggested that the illusion would only occur if weighting of conflicting sensory information and their subsequent integration results in a statistically plausible compromise. In three different experiments, we investigated the role of distance between the two hands as well as their proximity to the body’s midline in influencing the occurrence of the illusion. Overall, the results suggest that the illusion is abolished when placing the two hands apart, therefore increasing the mismatch between the visual and proprioceptive modality, whereas the proximity of the two hands to the body’s midline plays only a minor role on the subjective report of the illusion. This might be driven by the response properties of visuotactile bimodal cells encoding the peripersonal space around the hand.
On Combining IR Methods to Improve Bug Localization

Information Retrieval (IR) methods have been recently employed to provide automatic support for bug localization tasks. However, for an IR-based bug localization tool to be useful, it has to achieve adequate retrieval accuracy. Lower precision and recall can leave developers with large amounts of incorrect information to wade through. To address this issue, in this paper, we systematically investigate the impact of combining various IR methods on the retrieval accuracy of bug localization engines. The main assumption is that different IR methods, targeting different dimensions of similarity between artifacts, can be used to enhance the confidence in each others' results. Five benchmark systems from different application domains are used to conduct our analysis. The results show that a) near-optimal global configurations can be determined for different combinations of IR methods, b) optimized IR-hybrids can significantly outperform individual methods as well as other unoptimized methods, and c) hybrid methods achieve their best performance when utilizing information-theoretic IR methods. Our findings can be used to enhance the practicality of IR-based bug localization tools and minimize the cognitive overload developers often face when locating bugs.
Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.
FAST-ODT: A Lightweight Outlier Detection Scheme for Categorical Data Sets

Outlier detection is a key data analysis technique that aims to find unusual data objects in a data set. It has been widely used in varied areas, including communication networks, finance, medicine, environmental studies, etc. Many applications in these areas involve categorical data. For example, the data set used in the application of intrusion detection normally includes a group of captured packets, which tend to have categorical attributes such as “protocol”. Although there are many outlier detection algorithms for applications involving numerical data, only a few existing schemes can handle categorical data. And the schemes designed for categorical data seriously suffer from two problems: low detection precision and high time complexity. In this paper, we present two novel outlier detection algorithms for categorical data sets. First of all, we describe a simple scheme based on entropy, Outlier Detection Tree (ODT). With ODT, a classification tree is constructed to classify the data set into two classes: a normal class and an abnormal class. Thereafter, each data object is identified as an outlier or a normal one using the if-then rules in the tree. Furthermore, we propose an advanced outlier detection algorithm, FAST-ODT, which achieves both high detection accuracy and low time complexity. Our experimental results indicate that FAST-ODT outperforms the existing algorithms in terms of outlier detection precision and computational complexity.
Model Adaption Object Detection System for Robot

Object detection for robot guidance is a crucial mission for autonomous robots, which has provoked extensive attention for researchers. However, the changing view of robot movement and limited available data hinder the research in this area. To address these matters, we proposed a new vision system for robots, the model adaptation object detection system. Instead of using a single one to solve problems, We made use of different object detection neural networks to guide the robot in accordance with various situations, with the help of a meta neural network to allocate the object detection neural networks. Furthermore, taking advantage of transfer learning technology and depthwise separable convolutions, our model is easy to train and can address small dataset problem.
Mobile vocabulometer

Vocabulary acquisition is the basis of learning a language, and using flashcards applications is a popular method for learners to memorize the meaning of unknown words. Unfortunately, this method alone is not effective for learners to remember the meaning of words when they appear in sentences. To solve this, we developed the Mobile Vocabulometer which allows users to acquire new vocabulary with context-based learning. Based on the correlation between comprehension and interests, we use the learning materials that adapt to users' interests and language skills. This system harnesses the power of the original Vocabulometer, and modifies it to be effective for mobile learning. An experiment on Japanese university students showed that, overall, learners achieved better results compared to using a simple flashcard application. This result indicates that this system provides a significant advantage over context-free learning systems.
Real-Time Sign Language Recognition Based on Video Stream

There are millions of deaf-dumb people in the world communicating by sign language, thus designing a sign language recognition system is very meaningful and valuable for normal people to understand them. In this paper, we investigate a real-time Chinese sign language recognition system. A Chinese sign language dataset is firstly created. Considering practical applications, RGB camera is used to collect video stream, instead of RGB-D camera. In order to improve the accuracy of recognition, we propose a 3D-CNN method combined with optical flow processing. The collected RGB video stream is processed by optimized dense optical flow, and then put into 3D-CNN to extract feature vectors. For practical considerations, a real-time sign language recognition system is designed, composed of artificial interaction interface, motion detection module, hand and head detection module, etc. Experimental results show the superb performance and the applicability of the proposed systems.
Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis

A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed representation in the context of matching image patches for registration applications and for learning a bag of visual words for whole slide image summarization.
A Time-Aware Graph Neural Network for Session-Based Recommendation

Recently, Graph Neural Networks (GNNs) have attracted increasing attention in the field of session-based recommendation, due to its strong ability on capturing complex interactive transitions within sessions.However, existing GNN-based models either lack the use of user's long-term historical behaviors or fail to address the impact of collaborative filtering information from neighbor users on the current session, which are both important to boost recommendation.In addition, previous work only focuses on the sequential relations of interactions while neglects the time interval information which can imply the correlations between different interactions.To tackle these problems, we propose a Time-Aware Graph Neural Network (TA-GNN) for session-based recommendation.Specifically, we first construct a user behavior graph by linking the interacted items of the same user according to their corresponding time order.A time-aware generator is designed to model the correlations between different nodes of the user behavior graph by considering the time interval information.Moreover, items from the neighbor sessions of the current session are selected to build a neighborhood graph.Then the two graphs are respectively processed by two different modules to learn the representation of the current session, which is applied to produce the final recommendation list.Comprehensive experiments show that our model outperforms stateof-the-art baselines on three real world datastes.We also investigate the performance of TA-GNN on different numbers of historical interactions and on different session length, finding that our model presents consistently advantages under different conditions.
A Comparison of Kalman Filter-based Approaches for Elliptic Extended Object Tracking

In this work, we discuss and compare Kalman filter-based approaches for tracking an elliptic extended object parameterized with orientation and semi-axes lengths. The methods include an Extended Kalman filter (EKF) implementation of the Random Hypersurface Model (RHM) approach using a radial function, an EKF-based approach for the Multiplicative Error Model (MEM), called MEM-EKF*, and a method for tracking the semi-axes independently, the Independent Axes Estimation (IAE) approach. We discuss pros and cons of the methods and compare them in various scenarios with a maneuvering object.
Knowledge Graphs to Empower Humanity-Inspired AI Systems

We present a theoretically motivated design perspective, challenges, and applications of next-generation artificial intelligence (AI) systems. We envision systems with greater capabilities for meaningful human interaction, including socially adaptive behavior that incorporates personalization and sensitivity to social context and intentionality. Personalized knowledge graphs combining generic, common-sense, and domain-specific knowledge with both sociocultural values and norms and individual cognitive models provide a foundation for building humanity-inspired AI systems.
Does BERT Understand Code? – An Exploratory Study on the Detection of Architectural Tactics in Code

Quality-driven design decisions are often addressed by using architectural tactics that are re-usable solution options for certain quality concerns. Creating traceability links for these tactics is useful but costly. Automating the creation of these links can help reduce costs but is challenging as simple structural analyses only yield limited results. Transfer-learning approaches using language models like BERT are a recent trend in the field of natural language processing. These approaches yield state-of-the-art results for tasks like text classification. In this paper, we experiment with treating detection of architectural tactics in code as a text classification problem. We present an approach to detect architectural tactics in code by fine-tuning BERT. A 10-fold cross-validation shows promising results with an average $$F_1$$ -Score of 90%, which is on a par with state-of-the-art approaches. We additionally apply our approach on a case study, where the results of our approach show promising potential but fall behind the state-of-the-art. Therefore, we discuss our approach and look at potential reasons as well as downsides and future work.
Fixed-Sign Binary Neural Network: An Efficient Design of Neural Network for Internet-of-Things Devices

High computational requirement and rigorous memory cost are the significant issues which limit Convolutional Neural Networks' deployability in resource-constrained environments typically found in edge devices of Internet-of-Things (IoT). To address the problem, binary and ternary networks have been proposed to constrain the weights to reduce computational and memory costs. However, owing to the binary or ternary values, the backward propagations are not as efficient as normal during training, which makes it tough to train in edge devices. In this paper, we find a different way to resolve the problem and propose Fixed-Sign Binary Neural Network (FSB), which decomposes convolution kernel into sign and scaling factor as the prior researches but only trains the scaling factors instead of both. By doing so, our FSB avoids the sign involved in backward propagations and makes models easy to be deployed and trained in the IoT devices. Meanwhile, the convolution-acceleration architecture which we design for our FSB results in a reduced computing burden while achieving the same performance. Thanks to the efficiency of our FSB, even though we randomly initialize the sign and fix it to be untrainable, our FSB still has remarkable performances.
Extended flag gadgets for low-overhead circuit verification

Flag verification techniques are useful in quantum error correction for detecting critical faults. Here we present an application of flag verification techniques to improving post-selected performance of near-term algorithms. We extend the definition of what constitutes a flag by creating error-detection gadgets based on known transformations of unitary operators. In the case of Clifford or near-Clifford circuits, these unitary operators can be chosen to be controlled Pauli gates, leading to gadgets which require only a small number of additional Clifford gates. We show that such flags can improve circuit fidelities by up to a factor of 2 after post selection, and demonstrate their effectiveness over error models featuring single-qubit depolarizing noise, crosstalk, and two-qubit coherent overrotation.