Reviewerly/exHarmony huggingface dataset:
EARLIN: Early Out-of-Distribution Detection for Resource-Efficient Collaborative Inference

Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs (e.g., images) to a server (i.e., cloud) where the heavy deep learning models run. While this setup works cost-effectively for successful inferences, it severely underperforms when the model faces input samples on which the model was not trained (known as Out-of-Distribution (OOD) samples). If the edge devices could, at least, detect that an input sample is an OOD, that could potentially save communication and computation resources by not uploading those inputs to the server for inference workload. In this paper, we propose a novel lightweight OOD detection approach that mines important features from the shallow layers of a pretrained CNN model and detects an input sample as ID (In-Distribution) or OOD based on a distance function defined on the reduced feature space. Our technique (a) works on pretrained models without any retraining of those models, and (b) does not expose itself to any OOD dataset (all detection parameters are obtained from the ID training dataset). To this end, we develop EARLIN (EARLy OOD detection for Collaborative INference) that takes a pretrained model and partitions the model at the OOD detection layer and deploys the considerably small OOD part on an edge device and the rest on the cloud. By experimenting using real datasets and a prototype implementation, we show that our technique achieves better results than other approaches in terms of overall accuracy and cost when tested against popular OOD datasets on top of popular deep learning models pretrained on benchmark datasets.
Analyzing the Behaviour of Java–Based Movie Recommendation System using Machine Learning

Everyone like and enjoy watching movies. Movies have tremendous glamour and unique magic to educate, entertain, and influence the spectators to a great extent. The need is to take positive aspects from movies instead of the negative sides that may harm our society. In the current scenario, the people are more considerate on the rating points of any particular item that is available online or offline before investing money in it. Movies are also not unaffected by this fact of rating and recommendation policies. So, there is a need to implement a recommender system that can recommend a suitable movie to the viewers. In this perspective, this research work has implemented a movie recommendation system by considering the latent factor. This system is coded in Java programming language. The implemented application can be commercially used since it helps the viewers to select the movies of their own choice.
All Factors Should Matter! Reference Checklist for Describing Research Conditions in Pursuit of Comparable IVR Experiments

A significant problem with immersive virtual reality (IVR) experiments is the ability to compare research conditions. VR kits and IVR environments are complex and diverse but researchers from different fields, e.g. ICT, psychology, or marketing, often neglect to describe them with a level of detail sufficient to situate their research on the IVR landscape. Careful reporting of these conditions may increase the applicability of research results and their impact on the shared body of knowledge on HCI and IVR. Based on literature review, our experience, practice and a synthesis of key IVR factors, in this article we present a reference checklist for describing research conditions of IVR experiments. Including these in publications will contribute to the comparability of IVR research and help other researchers decide to what extent reported results are relevant to their own research goals. The compiled checklist is a ready-to-use reference tool and takes into account key hardware, software and human factors as well as diverse factors connected to visual, audio, tactile, and other aspects of interaction.
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation. In this work, we tackle two defects that hinder their deployment in real-world applications: 1) Predictions lack uncertainty quantification that may be crucial to many decision-making systems; 2) Large memory storage and computational consumption demanding extensive hardware resources. To address these issues and improve their practicality we demonstrate a few-parameter compact Bayesian convolutional architecture, that achieves a marginal improvement in accuracy in comparison to related work using significantly fewer parameters and compute operations. The architecture combines parameter-efficient operations such as separable convolutions, bilinear interpolation, multi-scale feature propagation and Bayesian inference for per-pixel uncertainty quantification through Monte Carlo Dropout. The best performing configurations required fewer than 2.5 million parameters on diverse challenging datasets with few observations.
Quality-Aware Distributed Computation and Communication Scheduling for Fast Convergent Wireless Federated Learning

In wireless federated learning (WFL), machine learning (ML) models are trained distributively on wireless edge devices without the need of collecting data from the devices. In such a setting, the quality of a local model update heavily depends on the variance of the local stochastic gradient, determined by the mini-batch data size used to compute the update. In this paper, we explore quality-aware distributed computation for WFL where user devices share limited communication resources, using mini-batch size as a "knob" to control the quality of users&#x2019; local updates. In particular, we study joint mini-batch size design and communication scheduling, with the goal of minimizing the training loss as well as the training time of the FL algorithm. For the case of IID data, we first characterize the optimal communication scheduling and the optimal minibatch sizes. Then we develop a greedy algorithm that finds the optimal set of participating users with an approximation ratio. For the case of non-IID data, we first characterize the optimal communication structure and the optimal mini-batch sizes. Then we develop algorithms that find the optimal communication order for some special cases. Our findings provide useful insights for the computation-communication co-design for WFL. We evaluate the proposed mini-batch size design and communication scheduling using simulations, which corroborate improved learning accuracy and learning time.
Industrial Intrusion Detection Technology Based on One-dimensional Multi-scale Residual Network

In order to solve the problem that the traditional intrusion detection algorithm cannot learn more information based on the traffic data effectively and the detection accuracy is not ideal, an intrusion detection algorithm based on the one-dimensional multi-scale residual network for industrial control systems is proposed. Firstly, the nondimensionalization of input data is realized by defining the centrosymmetric logarithmic function. Then, a one-dimensional multi-scale residual neural network model is constructed to learn the characteristic information of industrial control data, and through cross-validation, parameter tuning is realized to obtain the best model. The experimental results show that the accuracy of this method is 98.99% and the AUC score is 0.9984, which can effectively realize the intrusion detection function under the industrial control system.
Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization

Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast variation problem effectively compared to Histogram Equalization (HE), Difference of Gaussian (DoG), and Butterworth Homomorphic Filtering (BHF). Seven (7) types of binarization methods were tested on the corrected image and produced a positive and impressive result. Result: Finally, a comparison in terms of Signal Noise Ratio (SNR), Misclassification Error (ME), F-measure, Peak Signal Noise Ratio (PSNR), Misclassification Penalty Metric (MPM), and Accuracy was calculated. Each binarization method shows an incremented result after applying it onto the corrected image compared to the original image. The SNR result of our proposed image is 9.350 higher than the three (3) other methods. The average increment after five (5) types of evaluation are: (Otsu = 41.64%, Local Adaptive = 7.05%, Niblack = 30.28%, Bernsen = 25%, Bradley = 3.54%, Nick = 1.59%, Gradient-Based = 14.6%). Conclusion: The results presented in this paper effectively solve the contrast problem and finally produce better quality images.
ABC: Attention with Bounded-memory Control

Transformer architectures have achieved stateof-the-art results on a variety of natural language processing (NLP) tasks.However, their attention mechanism comes with a quadratic complexity in sequence lengths, making the computational overhead prohibitive, especially for long sequences.Attention context can be seen as a random-access memory with each token taking a slot.Under this perspective, the memory size grows linearly with the sequence length, and so does the overhead of reading from it.One way to improve the efficiency is to bound the memory size.We show that disparate approaches can be subsumed into one abstraction, attention with bounded-memory control (ABC), and they vary in their organization of the memory.ABC reveals new, unexplored possibilities.First, it connects several efficient attention variants that would otherwise seem distinct.Second, this abstraction gives new insights-an established approach (Wang et al., 2020b) previously thought to not be applicable in causal attention, actually is.Last, we present a new instance of ABC, which draws inspiration from existing ABC approaches, but replaces their heuristic memory-organizing functions with a learned, contextualized one.Our experiments on language modeling, machine translation, and masked language model finetuning show that our approach outperforms previous efficient attention models; compared to strong transformer baselines, it significantly improves the inference time and space efficiency with no or negligible accuracy loss.
Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction

Inspired by quantum-like phenomena in human language understanding, recent studies propose quantum probability-inspired neural networks to model natural language by treating words as superposition states and a sentence as a mixed state. However, many complex natural language processing tasks (e.g., emotion–cause pair extraction or joint dialog act recognition and sentiment classification) require modeling the complex and graphical interaction of multiple text pieces (e.g., multiple clauses in a document or multiple utterances in a dialog). The existing quantum probability-inspired neural networks only encode sequential interaction of a sequence of words, but cannot model the complex interaction of text pieces. To generalize the quantum framework from modeling word sequence to modeling complex and graphical text interaction, we propose a Quantum Probability-inspired Graph Attention NeTwork (QPGAT) by combining quantum probability and graph attention mechanism in a unified framework. Specifically, a text interaction graph is firstly constructed to describe the complex interaction of text pieces. Then QPGAT models each text node as a particle in a superposition state and each node’s neighborhood in the graph as a mixed system in a mixed state to learn interaction-aware text node representations. We apply QPGAT to the two important and complex NLP tasks, emotion–cause pair extraction and joint dialog act recognition and sentiment classification. Experiment results show that QPGAT is competitive compared with the state-of-the-art methods on the two complex NLP tasks, demonstrating the effectiveness of QPGAT. Moreover, QPGAT can also provide a reasonable post-hoc explanation about the model decision process for emotion–cause pair extraction.
Enhancing structure modeling for relation extraction with fine-grained gating and co-attention

Relation extraction is a critical natural language processing task. Existing dependency-based models captured long-range syntactic relations, but they usually cannot fully exploit information from sentences. They often used hand-crafted rules to prune redundant edges from dependency trees, but suffer from the imbalance of including and removing contents. When incorporating sequence models, they usually ignored the semantic and syntactic interactions between words. In this paper, we propose to automatically learn relational dependency structures with a fine-grained gating strategy. We decompose the dependency tree into differently informative parts and apply different gating methods to each part. To further capture the word-level interactions, we propose to apply the co-attention mechanism to combine structure and sequence models. We apply a neural network to learn the affinity matrix and derive mutual attention weights between semantic and syntactic representations. We conduct experiments on two benchmark datasets and the results indicate the effectiveness of our method.
Remote Assistance through Augmented Reality for the Electric Power Workforce

The utilities workforce plays an important role for the daily functioning and running of a city. For this reason increasing its efficiency and improving its services with technological trends, proves to be a very effective solution for both the utility and the society. Since Augmented Reality (AR) presents a large number of applicabilities and it is being used more and more for industrial purposes, it can be implemented by using it to support employees' communication and assistances. This paper aims to evaluate this applicability of AR to Remote Assistance (RA). It will present a practical use case by an R&D project, named Urban Futurability, developed in a Brazilian Electrical Distribution Company, where the main system used for the RA is the Microsoft Dynamics Remote Assist. Innovative functionalities are evaluated according to the results brought by the early RA application. Furthermore, the paper brings an initial cost benefit analysis for its application, aiming for a roll out implementation based on the Return of Investment (ROI).
Fast ObjectRank for Large Knowledge Databases

ObjectRank is an essential tool to evaluate an importance of nodes for a user-specified query in heterogeneous graphs. However, existing methods are not applicable to massive graphs because they iteratively compute all nodes and edges. This paper proposes SchemaRank, which detects the exact top-k important nodes for a given query within a short running time. SchemaRank dynamically excludes unpromising nodes and edges, ensuring that it detects the same top-k important nodes as ObjectRank. Our extensive evaluations demonstrate that the running time of SchemaRank outperforms existing methods by up to two orders of magnitude.
Wild by Design: Workshop on Designing Ubiquitous Health Monitoring Technologies for Challenging Environments

Recent years have shown an emergence of ubiquitous technologies that aim to monitor a person’s health in their day to day. However, albeit focused at a real world setting and technically able, most research is still limited in its real-world coverage, suitability, and adoption. In this workshop, we will focus on the challenges of real world health monitoring deployments to produce forward-looking insights that can shape the way researchers and practitioners think about health monitoring, in platforms and systems that account for the complex environments where they are bound to be used.
A Brief Roadmap into Uncertain Knowledge Representation via Probabilistic Description Logics

Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge—often extensions of classical knowledge representation languages—have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps.
Proposal of Extracting State Variables and Values from Requirement Specifications in Japanese by using Dependency Analysis

A requirement specification for software is usually described in a natural language and thus may include sentences containing ambiguity and contradiction. Design errors due to ambiguous expressions and contradictions are often found later in the development process. These ambiguous sentences would force the developer to go back to the design process again. In order to prevent this kind of rework, a method of automatically converting a required specification written in Japanese to a state transition model is desired to help detect ambiguity and contradiction points of the specification. In previous studies, we have improved the extraction accuracy by refining the parsing rules. However, in order to parse a variety of sentences, it is necessary to set up new rules or refine the rules. In this paper, we propose a method of using a dependency analyzer in syntactic parsing. Using dependency analyzers eliminates the need for both of rule setting and refinement. Thus, the disadvantage would be eliminated. In addition, the effectiveness of the proposed method is reported by extracting and showing the elements necessary to create a state transition diagram.
Genetic Algorithm Assisted State-Recovery Attack on Round-Reduced Xoodyak

Genetic algorithm (GA) has led to significant improvements in many challenging tasks, including combinatorial optimization, signal processing, and artificial life. It shows enormous potential for cryptanalysis. This paper designed a heuristic algorithm based on GA for the known-plaintext attack on round-reduced Xoodyak, a finalist of the NIST lightweight cryptography project, under the nonce-respecting setting. To accomplish this, we firstly remodel Xoodoo, the underlying permutation of Xoodyak, portraying it as a function whose input and output are continuous variables defined in [0, 1], representing the likelihood that each bit is equal to 1 and describing the goal of cryptanalysis as an objective function optimized with GA secondly. Consequently, we can abstract the potential information of the unknown state of Xoodyak from the results given by GA. Compared with traditional methods, ours requires less knowledge about complex cryptanalysis as GA can work well with lower complexity, both in time complexity and data complexity, and can be carried out under more restricted conditions.
Garbage detection and classification based on improved YOLOV4

Abstract Object detection is an important research task in computer vision. With environment problems becoming seriously, garbage detection has become a hot direction in object detection. Though great progress has been made in garbage detection, there still exists challenges for general-purpose detectors with no public datasets and low recognition accuracy rate. Based on YOLOV4 model, this paper proposes an improved YOLOV4 detector for garbage detection. Specifically, CBAM is added to the feature extraction network in order to better extract features in deep networks, and focal function is integrated into the loss function to improve class imbalance. When preparing the data, environment information and random information are added to pictures to simulate the distribution of garbage in real environment, and finally an urban household garbage dataset containing 47 classes of 45,910 images named TrashSet has been produced. Experiments results on TrashSet verify that our detector has a considerable performance, and mAP reaches 97.15%.
Vision-based Scene Analysis toward Dangerous Cycling Behavior Detection Using Smartphones

Cycling can contribute to improvements of user’s physical and mental health, and can also bring positive impacts on the environment. However, many riders do not necessarily have a full understanding of safe bicycle riding. Existing sensing technology and mobile systems have demonstrated assistive features for car driving, and this motivates us to design a mobile application to feedback cyclist’s bicycle riding. In this paper, we present the current progress of our mobile system to perform vision-based scene analysis tailored toward the detection of potentially dangerous cycling.
Histopathological Image Classification Using Ensemble Transfer Learning

Histopathological image analysis of biopsy sample provides an accurate diagnosis method for cancer. Usually Pathologists examine the microscopic images of biopsy sample manually for the detection and grading of cancer. Automation in this field helps the pathologists to take a second opinion before confirming with their findings. We propose an effective method of automated cancer detection by combining the effect of transfer learning and ensemble learning. Six pre-trained models such as Densenet121, Resnet 50, Xception, EfficientNet B7, MobileNetV2, and VGG19 are used for preparing an ensemble model. A dataset contains 5547 H&E stained histopathological images of malignant and benign tissues are used to train and validate each models individually and obtained an accuracy of 77.9%, 79%, 79.8%, 78.3%, 77%, and 76% respectively. Based on the accuracy, best performing three models Resnet50, Xception, and EfficientnetB7 are selected to form an ensemble model. Then the layers and weights of these models are freezed and the output layers are concatenated to make an ensemble model. New dense layers are added to the ensembled model to provide a single output for binary classification. The model is compiled by an Adam optimizer with a learning rate of 0.001. The images are again applied to this ensemble model to classify the malignant and benign tissues and obtained an accuracy of 96% and precision of 96%.
Learning Structural Representations for Recipe Generation and Food Retrieval

Food is significant to human daily life. In this paper, we are interested in learning structural representations for lengthy recipes, that can benefit the recipe generation and food cross-modal retrieval tasks. Different from the common vision-language data, here the food images contain mixed ingredients and target recipes are lengthy paragraphs, where we do not have annotations on structure information. To address the above limitations, we propose a novel method to unsupervisedly learn the sentence-level tree structures for the cooking recipes. Our approach brings together several novel ideas in a systematic framework: (1) exploiting an unsupervised learning approach to obtain the sentence-level tree structure labels before training; (2) generating trees of target recipes from images with the supervision of tree structure labels learned from (1); and (3) integrating the learned tree structures into the recipe generation and food cross-modal retrieval procedure. Our proposed model can produce good-quality sentence-level tree structures and coherent recipes. We achieve the state-of-the-art recipe generation and food cross-modal retrieval performance on the benchmark Recipe1M dataset.