Reviewerly/exHarmony huggingface dataset:
Similitude Attentive Relation Network for Click-Through Rate Prediction

In online advertising systems, having a good knowledge of user behavior is crucial for click-through rate (CTR) prediction. In recent years, many researchers turn to seek a better way of user representation by modeling the behavior sequences with recurrent neural network (RNN). However, recurrent layers implicitly adopt the assumption that elements with different orders are fundamentally different, which is inefficient in many practical scenarios with much uncertainty and complicated hidden states. In this paper, we follow the paradigm of Relation Network (RN), and propose a new model called Similitude Attentive Relation Network (SARN). The user behavior is modeled as a graph, where nodes correspond to the visited items and edges correspond to the relations. To capture the latent user interest better, the model concentrates on the relations between items, rather than the translation on the time series. More specifically, the model tries to learn the similarity between items in a semantic space through a learnable dot-product operation and blend both of the item representations and relational information together as the final relations. We define our user representation on an attentive pooling of the relations directly. To verify the effectiveness of our method, extensive experiments on two public datasets and one real-world online advertising dataset are conducted. Experimental results show that our methods achieve usually better performance than others. Besides, we explore the properties of our model by controlled experiments and show the learned relational knowledge by visualizing the inner states of SARN.
PARC: A Plan and Activity Recognition Component for Assistive Robots

Mobile robot assistants have many applications, such as helping people in their daily living activities. These robots have to detect and recognize the actions and goals of the humans they are assisting. While there are several wide-spread plan and activity recognition solutions for controlled environments with many built-in sensors, like smart-homes, there is a lack of such systems for mobile robots operating in open settings, such as an apartment. We propose a module for the recognition of activities and goals for daily living by mobile robots, in real time and for complex activities. Our approach recognizes human-object interaction using an RGB-D camera to infer low-level actions which are sent to a goal recognition algorithm. Results show that our approach is both in real time and requires little computational resources, which facilitates its deployment on a mobile and low-cost robotics platform.
Encoding High-Throughput Jpeg2000 (Htj2k) Images On A Gpu

High-Throughput JPEG2000 (HTJ2K) is a new addition to the JPEG2000 suite of coding tools; it has been recently approved as Part-15 of the JPEG2000 standard, and the JPH file extension has been designated for it. The HTJ2K employs a new “fast” block coder that can achieve higher encoding and decoding throughput than a conventional JPEG2000 (C-J2K) encoder. The higher throughput is achieved because the HTJ2K codec processes wavelet coefficients in a smaller number of steps than C-J2K. Moreover, the HTJ2K block coder is also more amenable to parallelizable high-speed software and hardware implementations. The HTJ2K retains most of the features and capabilities of JPEG2000, and it also supports lossless transcoding between HTJ2K and already compressed C-J2K images. Quality scalability however is more limited than C-J2K. In a recent work, we presented preliminary performance results for decoding HTJ2K images on a GPU. In this work, we present a GPU-based HTJ2K encoder; we also present early encoding results for such an encoder, showing that it is possible to encode 4K4:4:4 HDR videos at more than 70 frames per second (fps) on a low-end card, while a high-end GPU can encode such videos at more than 400 fps.
ClaVis

We propose ClaVis, a visual analytics system for comparative analysis of classification models. ClaVis allows users to visually compare the performance and behavior of tens to hundreds of classifiers trained with different hyperparameter configurations. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that reveals correlations between parameters and scores, and a training history chart. We demonstrate the effectivity of our approach in multiple case studies for training classification models in the domain of natural language processing.
Spatial distribution of information effective for logic function learning in spin-wave reservoir computing chip utilizing spatiotemporal physical dynamics

This paper investigates the spatial distribution of information effective for function learning in a spin-wave reservoir-computing garnet chip. We map the neural weights of a readout neuron virtually connected massively and densely to the reservoir chip. We find that the spatial weight distribution shows wavefront-like lines, suggesting the importance of concurrent and time-different interferences of the spin waves. We also estimate the size of reservoir output electrodes required for the proper information extraction. These results are significantly useful for designing spin reservoir chips in the near future energy efficient devices.
Investigation of Image Classification Using HOG, GLCM Features, and SVM Classifier

Recently, in order to solve the problem of image classification, some image features and classifiers play more and more important role in the related research field. This article investigates an image classification method by the histogram of oriented gradient (HOG) features, the gray-level co-occurrence matrix (GLCM) features, and the support vector machine (SVM) classifier. By obtaining the HOG and the GLCM features of image, the combination of them is inputted into the SVM for the training and the test. The experiment results have manifested the effectiveness of the proposed method. The use of the combination of HOG features and GLCM features in image classification is far superior to the use of them alone.
Blockchain Consensus Mechanisms and Their Applications in IoT: A Literature Survey

With the advance of blockchain technology, blockchain has been used in many fields. As a point-to-point network with characteristics of traceability, decentralization, de-trust and so on, blockchain technology is suitable for deploying Internet of Things networks. It provides the opportunity for the prosperity of IoT networks. As a key component of blockchain, blockchain consensus mechanism is worth studying. In this paper, we systematically survey blockchain consensus mechanisms from the perspective of Internet of Things networks requirements. We first introduce the requirements of consensus mechanisms in IoT networks to help researchers understand the connection of blockchain consensus mechanisms and IoT. Then we divide the blockchain consensus mechanism into four categories, namely, consensus mechanisms for security, consensus mechanisms for scalability, consensus mechanisms for energy saving, and consensus mechanisms for performance improvement. We further analyze those blockchain consensus mechanisms and point out the potential direction of blockchain consensus designs based on our observation. The target of this paper is to provide insights for researchers into the future development of blockchain consensus mechanisms in IoT and encourage more efforts in this field.
Predicting Human Errors from Gaze and Cursor Movements

Intelligent interfaces are increasingly integrated into diverse technological areas. In complex high-risk environments, where humans represent a crucial part of the system and their attention is often divided between simultaneous activities, imminent human errors may have serious consequences. Enhancing interfaces with predictive capabilities promotes the safe and reliable operation of such systems. In this work, we employ a data-driven approach to predict human errors in a special divided attention task involving timing constraints and requiring focused concentration and frequent shifts of attention. We performed a longitudinal study with 10 subjects, and constructed time series from the experimental data using gaze movement and mouse cursor motion features in order to classify successful and failed actions. We evaluate classical machine learning algorithms, compare them with a more traditional temporal modeling approach and a deep learning based LSTM model. Employing a leave-one- subject-out cross-validation procedure we achieve a classification accuracy of up to 86%, with LSTM presenting the highest performance. Furthermore, we investigate the trade-off between evaluation metrics and anticipation window, i.e. the time remaining until the correct action can still be performed. We conclude that prediction is feasible and accuracy and F1-score increases, despite the training dataset becoming greatly imbalanced. Investigating the anticipation window allows to understand how far in advance human errors need to be predicted in order to initiate preventive measures. Our efforts have implications for the design of predictive interfaces involving decision making under time pressure in dynamic divided attention environments.
Multi-Grained Selection and Fusion for Fine-Grained Image Representation

How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.
Deep Feature Compression With Spatio-Temporal Arranging for Collaborative Intelligence

Collaborative Intelligence is a new paradigm that splits a deep neural network (DNN) into an edge DNN and a cloud DNN. In this paradigm, deep features, which are the outputs of the edge DNN, are compressed and transmitted to the cloud DNN. Since the deep features have a few responses that are similar to each other, previous studies have proposed compressing them as an image with a spatial arrangement to utilize spatial correlation between the deep features. However, this method may not sufficiently consider the similarity because only the spatial correlation is utilized. In this work, we propose a “spatio-temporal arranging” that considers the similarity of deep features in both the spatial and temporal directions. This method arranges the deep features spatiotemporally and compresses them as a video to utilize the spatio-temporal correlation. We perform spatial arranging as images to increase the spatial correlation and temporal arranging as a video with a novel ordering search to increase the temporal correlation. Experimental results demonstrate that our method performs better than previous methods.
Adaptive Pooling Is All You Need: An Empirical Study on Hyperparameter-insensitive Human Action Recognition Using Wearable Sensors

A plethora of techniques have been proposed in human action recognition fields, and particularly deep learning-based methods such as convolutional neural networks (CNNs) have achieved impressive results. Usually, there is need to tune hyper-parameters in the deep neural network (e.g., filter size, stride) to achieve reasonable results. Such hyper-parameter tuning is, however, extremely time and resource-intensive even for small models. In this paper, we posit that the inclusion of an adaptive pooling in CNNs used for human action recognition largely eliminates the need for hyper-parameter tuning. Specifically, we demonstrated our idea for human action recognition using inertial sensor data (i.e., a temporal sequence) with a one-dimensional adaptive pooling. We compared the adaptive pooling to conventional CNNs with randomly chosen hyper-parameters using a publicly available data set for human action recognition. Experimental results showed that the adaptive pooling achieved better accuracy than the conventional CNNs.
Dynamic programming based fuzzy partition in fuzzy decision tree induction

Fuzzy decision trees are one of the most popular extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Among the majority of fuzzy decision trees learning methods, the number of fuzzy partitions is given in advance, that is, there are the same amount of fuzzy items utilized in each condition attribute. In this study, a dynamic programming-based partition criterion for fuzzy items is designed in the framework of fuzzy decision tree induction. The proposed criterion applies an improved dynamic programming algorithm used in scheduling problems to establish an optimal number of fuzzy items for each condition attribute. Then, based on these fuzzy partitions, a fuzzy decision tree is constructed in a top-down recursive way. A comparative analysis using several traditional decision trees verify the feasibility of the proposed dynamic programming based fuzzy partition criterion. Furthermore, under the same framework of fuzzy decision trees, the proposed fuzzy partition solution can obtain a higher classification accuracy than some cases with the same amount of fuzzy items.
The Infracrustal Rocks in the Egyptian Nubian Shield: An Overview and Synthesis

The infracrustal gneisses and related rocks are one of the most important rock units in the rock succession of the Egyptian Nubian Shield. Following the introduction (Sect. 5.1), the types of gneisses in the Egyptian Nubian Shield are discussed in Sect. 5.2. Section 5.3 deals with geochronological data of the gneisses in the ENS where most of the isotopic data of the granitoid gneisses from the Eastern Desert and Sinai support a juvenile Neoproterozoic origin of the gneiss domes. The Rock types of gneiss complex in the ENS area are presented in Sect. 5.4; followed by Sect. 5.5: Gneisses occurrences in the Egyptian Nubian Shield that include the Northern Eastern Desert (Barud, Um Tenassib and Abu Zawal), the Central Eastern Desert (Migif–Hafafit, Meatiq, Sibai, Shalul, Um Had, Shaitain, Gabal Zabara and El-Umra), the Southern Eastern Desert (Um Gunud, Beitan, Khuda, El-Hudi, Wadi Haimur–Abu Swayel, Abu Fas, Abu Rusheid–Sikait and Hamisana) and Sinai (Feiran–Solaf, Taba, Elat, Fjord, Wadi Watir, Qenaia and Wadi Kid); and finally, Sect. 5.6: the core complexes in the Egyptian Nubian Shield, discusses the controversy between existence and non-existence of the core complexes in the Egyptian Nubian Shield.
One-Shot Layer-Wise Accuracy Approximation For Layer Pruning

Recent advances in neural networks pruning have made it possible to remove a large number of filters without any perceptible drop in accuracy. However, the gain in speed depends on the number of filters per layer. In this paper, we propose a one-shot layer-wise proxy classifier to estimate layer importance that in turn allows us to prune a whole layer. In contrast to existing filter pruning methods which attempt to reduce the layer width of a dense model, our method reduces its depth and can thus guarantee inference speed up. In our proposed method, we first go through the training data once to construct proxy classifiers for each layer using imprinting. Next, we prune layers with smallest accuracy difference from their preceding layer till a latency budget is achieved. Finally, we fine-tune the newly pruned model to improve accuracy. Experimental results showed 43.70% latency reduction with 1.27% accuracy increase on CIFAR100 for the pruned VGG19. Further, we achieved 16% and 25% latency reduction with 0.58% increase and 0.01% decrease in accuracy respectively on ImageNet for ResNet-50. The major advantage of our proposed method is that these latency reductions cannot be achieved with existing filter pruning methods as they are bounded by the original model's depth. Code is available at https://github.com/selkerdawy/one-shot-layer-pruning.
Using usability and user experience scores to design an Augmented Reality-based Ambient Awareness interface to support spatially dispersed teams

One major determinant for the performance and productivity of teams is the temporal coordination of interdependent sub-tasks within the teamwork process. The accuracy of the temporal coordination significantly depends on whether the team member share a precise mental model of the teamwork process, which encompasses the current process state and the desired outcome. The more the assumptions of the individual team members about the current process state deviate, the less precise the temporal coordination of the team is. Looking at spatially dispersed teams, this challenge becomes even more complicated, since such teams have no naturally existing common cues, from which they can derive corresponding process relevant information. Therefore, this work presents a user-centered design approach to develop an Augmented Reality-based assistance system for the Microsoft HoloLens 1. Its goal is to support the temporal coordination of spatially dispersed teams by utilizing a method that can present information in an at-a-glance manner and helps the user to remain aware of the information without being overwhelmed or distracted by it, denoted Ambient Awareness. In order to develop a system with the highest possible team- and tasktechnology fit, we aggregated usability and user experience scores in a two-part user study with n $=$ 22 experts in the sense of a user-centered design process. To this end, we first determined the usability in a laboratory experimental setting. On the basis of the aggregated data, we identified three essential interface configuration clusters in the first step, which were evaluated subsequently according to their user experience in an online assessment. This two-step user-centered evaluation design allowed us to derive a user interface configuration that provides the highest team- and task-technology-fit under the given application-specific conditions.
On Research of Video Stream Detection Algorithm for Ship Waterline

The detection of the waterline of the ship mainly uses the manual observation of the water gauge scale to calculate the draft depth, which is easily affected by human factors. Therefore, this paper proposes a method based a video stream automatically detect the draft in view of the above problems. Firstly, data reprocessing is performed on each frame image in the video, and images smooth fuzzy is performed to obtain a grayscale image; Secondly, the inflection point operator is proposed to obtain the image edge feature, and the feature extraction kernel is used to detect the waterline. at the same time, the digitized image data is obtained, the water gauge scale value is segmented, and the feature is recognized by the deep neural network method. Finally, the draft is obtained by calculating the difference between the water scale value and the water line pixel. In this paper, several experiments are carried out on the computer to collect the video data. The experimental results show that the proposed kernel model has a high accuracy and strong robustness. At the same time, the interference caused by human factors is removed and the accuracy is improved.
Verifying object construction

In object-oriented languages, constructors often have a combination of required and optional formal parameters. It is tedious and inconvenient for programmers to write a constructor by hand for each combination. The multitude of constructors is error-prone for clients, and client code is difficult to read due to the large number of constructor arguments. Therefore, programmers often use design patterns that enable more flexible object construction---the builder pattern, dependency injection, or factory methods.
Exploration of 3D terrains using potential fields with elevation-based local distortions

Mobile robots can be used in numerous outdoor tasks such as patrolling, delivery and military applications. In order to deploy mobile robots in this kind of environment, where there are different challenges like slopes, elevations, or even holes, they should be able to detect such challenges and determine the best path to accomplish their tasks. In this paper, we are proposing an exploration approach based on potential fields with local distortions, in which we define preferences in uneven terrains to avoid high declivity regions without compromising the best path. The approach was implemented and tested in simulated environments, considering a ground robot embedded with two 2D LIDAR sensors, and the experiments demonstrated the efficiency of our method.
Dialog Modelling Experiments with Finnish One-to-One Chat Data

We analyzed two conversational corpora in Finnish: A public library question-answering (QA) data and a private medical chat dataẆe developed response retrieval (ranking) models using TF-IDF, StarSpace, ESIM and BERT methods. These four represent techniques ranging from the simple and classical ones to recent pretrained transformer neural networks. We evaluated the effect of different preprocessing strategies, including raw, casing, lemmatization and spell-checking for the different methods. Using our medical chat data, We also developed a novel three-stage preprocessing pipeline with speaker role classification. We found the BERT model pretrained with Finnish (FinBERT) an unambiguous winner in ranking accuracy, reaching 92.2% for the medical chat and 98.7% for the library QA in the 1-out-of-10 response ranking task where the chance level was 10%. The best accuracies were reached using uncased text with spell-checking (BERT models) or lemmatization (non-BERT models). The role of preprocessing had less impact for BERT models compared to the classical and other neural network models. Furthermore, we found the TF-IDF method still a strong baseline for the vocabulary-rich library QA task, even surpassing the more advanced StarSpace method. Our results highlight the complex interplay between preprocessing strategies and model type when choosing the optimal approach in chat-data modelling. Our study is the first work on dialogue modelling using neural networks for the Finnish language. It is also first of the kind to use real medical chat data. Our work contributes towards the development of automated chatbots in the professional domain.
Drcas: Deep Restoration Network For Hardware Based Compressive Acquisition Scheme

We propose a novel image acquisition scheme HCAS (Hardware based Compressed Acquisition Scheme) using hardware-based binning (downsampling), bit truncation and JPEG compression and develop a deep learning based reconstruction network DRCAS (Deep Restoration network for hardware based Compressed Acquisition Scheme) for images acquired using HCAS. DRCAS to our best knowledge is the first work proposed in the literature for the restoration of images acquired using acquisition scheme like HCAS. It is also superior in performance than state-of-the-art super resolution networks while being much smaller. We show that HCAS and DRCAS techniques will enable us to design much simpler and power efficient image acquisition pipelines.