Reviewerly/exHarmony huggingface dataset:
Thermal management of a 3D packaging structure for superconducting quantum annealing machines

Quantum annealing (QA) is a computing paradigm for solving combinatorial optimization problems by finding the low-energy configurations of complicated Ising models through quantum fluctuation. Recently, because of the strictly limited number of superconducting qubits per chip, three-dimensional (3D) integrated technologies have attracted great attention for realizing a practical large-scale QA machine. Because a cryogenic environment is required for qubit chip operation, the less-effective heat transfer in the stacked structure has become a major concern. In the present paper, a method to realize a sufficiently low temperature for quantum operation in large-scale superconducting QA machines based on 3D packaging technology is proposed and demonstrated numerically. We reveal that the heating in such a 3D structure, which seriously reduces the quantum coherence of qubit chips, is effectively prevented by adding through-silicon vias (TSVs). Accordingly, the temperature of the qubit chip is drastically reduced from 57.8 mK to 16.4 mK. Our results indicate that using TSVs are a powerful and simple approach to realizing practical large-scale superconducting QA machines.
Effects of ply thickness deviation and ply angle misalignment on the surface accuracy of CFRP laminates

This study examines two factors that affect the surface accuracy of symmetric carbon-fibre-reinforced plastic (CFRP) laminates: ply angle misalignment and ply thickness deviation. First, based on classic laminate theory, theoretical expressions for the effects of these factors are deduced. Then, COMSOL (FEM software) is used to analyse the relationship between these factors and the surface accuracy, and Zernike polynomials are used to fit the surface deformation of the CFRP laminates. It proves that ply angle misalignment and ply thickness deviation can cause astigmatic and power aberrations. Based on the integral constraint method in the form of Lagrangian multipliers, the rigid body displacement problem of the free thermal expansion simulation of CFRP laminates has been solved. Finally, three groups of experimental tests show that ply thickness deviation does occur. A ZYGO interferometer is used to observe the surface accuracy of the laminates, and it shows that astigmatism is the dominant aberration.
Answer-checking in Context: A Multi-modal Fully Attention Network for Visual Question Answering

Visual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image again for the final confirmation. In this paper, we mimic this process and propose a fully attention based VQA architecture. Moreover, an answer-checking module is proposed to perform a unified attention on the jointly answer, question and image representation to update the answer. This mimics the human answer checking process to consider the answer in the context. With answer-checking modules and transferred BERT layers, our model achieves the state-of-the-art accuracy 71.57% using fewer parameters on VQA-v2.0 test-standard split.
FashionGraph: Understanding fashion data using scene graph generation

Fashion analysis is an attractive domain for vision research due to its direct applications in e-commerce contexts. However, fashion datasets are commonly rather demanding, as both objects and attributes tend to be fine-grained and thus result in very long-tailed datasets. Furthermore, relationships between objects and attributes are often dense, but are crucial for the performance of fashion applications. In this paper, we propose to generate scene graphs for existing fashion datasets. By detecting relationships between fashion objects, their parts, and their attributes we gain a better understanding of the scenes. As no current fashion dataset provides scene graphs, we generate relationships between fashion objects from existing annotations. The output is post-processed and filtered to generate a meaningful scene graph for each image. In the experiments, we can show existing applications like image retrieval benefiting from the scene graph understanding. We first evaluate the accuracy of the generated scene graphs. Then, we employ scene graphs to fashion image retrieval in order to showcase their performance in real applications. The results show various benefits for fashion applications by exploiting scene graph knowledge. The resources and model for the proposed method are available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
InterRings: Towards Understanding Design Micro-games to Fit Daily Work Routine

Prior game researchers mostly focused on designing the ”macro” context which requires a decent proportion of time to engage and play, from which players may benefit. However, the demanding time and effort restrict playability during working hours. To our knowledge, there is little research exploring game design in a manner that enables momentary detachment from primary work for a mind refreshment and effortlessly resumes the suspended work after playing. In this paper, we propose a novel micro-game concept that enables the gamification of micro-breaks within working hours. To examine our concept, we adapted a conventional water-ring game into an interactive prototype, InterRings, which empowers users to play with handy objects. Our mixed observational studies and interviews revealed high acceptability and feasibility of the micro-game concept. Conclusively, three design guidelines are summarised for future development of micro-games.
Uncovering Abnormal Behavior Patterns from Mobility Trajectories

Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals’ activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns.
Order in the Court: Explainable AI Methods Prone to Disagreement.

By computing the rank correlation between attention weights and feature-additive explanation methods, previous analyses either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience. To investigate whether this approach is appropriate, we compare LIME, Integrated Gradients, DeepLIFT, Grad-SHAP, Deep-SHAP, and attention-based explanations, applied to two neural architectures trained on single- and pair-sequence language tasks. In most cases, we find that none of our chosen methods agree. Based on our empirical observations and theoretical objections, we conclude that rank correlation does not measure the quality of feature-additive methods. Practitioners should instead use the numerous and rigorous diagnostic methods proposed by the community.
A novel framework for delivering static search capabilities to large textual corpora directly on the Web domain: an implementation for Migne’s Patrologia Graeca

Purpose This study aims to provide a system capable of static searching on a large number of unstructured texts directly on the Web domain while keeping costs to a minimum. The proposed framework is applied to the unstructured texts of Migne’s Patrologia Graeca (PG) collection, setting PG as an implementation example of the method. Design/methodology/approach The unstructured texts of PG have automatically transformed to a read-only not only Structured Query Language (NoSQL) database with a structure identical to that of a representational state transfer access point interface. The transformation makes it possible to execute queries and retrieve ranked results based on a specialized application of the extended Boolean model. Findings Using a specifically built Web-browser-based search tool, the user can quickly locate ranked relevant fragments of texts with the ability to navigate back and forth. The user can search using the initial part of words and by ignoring the diacritics of the Greek language. The performance of the search system is comparatively examined when different versions of hypertext transfer protocol (Http) are used for various network latencies and different modes of network connections. Queries using Http-2 have by far the best performance, compared to any of Http-1.1 modes. Originality/value The system is not limited to the case study of PG and has a generic application in the field of humanities. The expandability of the system in terms of semantic enrichment is feasible by taking into account synonyms and topics if they are available. The system’s main advantage is that it is totally static which implies important features such as simplicity, efficiency, fast response, portability, security and scalability.
Trajectory optimization of wheeled mobile robot (WMR) in the wall-type arena

Over the last few decades, navigational motion synthesis in cluttered arena has remained as one of the most challenging area of investigation. In this paper, a smart neural network (SNN) based navigation control architecture for a wheeled mobile robot (WMR) has been presented. Here, two responsive activities are considered as the input function of the SNN controller. The obstacle position and obstacle angle with reference to the target location are two activities followed by the WMR during the trajectory planning in an environment. The outputs for the controller are wheel velocity and steering angle followed by target direction. To minimize the errors of the weight function, the backpropagation training architecture has been analyzed. It has been observed that, the robot generates a quick reaction time by using the SNN methodologies as compared to other methodologies. To evaluate the effectiveness of the developed controller, the proposed architecture has been tested in both simulation and experimental platforms keeping common environmental conditions such as position of source, target and obstacles. The results obtained from both of the platforms have been compared in terms of selected navigational parameters and the deviation obtained is found to be within 8%. The V-Rep simulation software has been used for the simulation experiments and Khepera 3 mobile robot has been used for real time experiments.
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.
An Implementation and Combining of Hybrid and Content Based and Collaborative Filtering Algorithms for the Higher Performance of Recommended Sytems

This article tells about the RS categories and HRS concepts with block diagram and finding out similarity metrices by using the equations and and understanding the datasets and dividing the Train/Test data and road map of hybrid algorithm and categories of algorithms used in RS and how to build HRS and method of combining CB and CF and Hybrid algorithm with customized algorithm by implementing it and evaluating the algorithms accuracy, sparsity and diversity and making a experimental setup on the the SurpriseLib library and loading of non identical algorithms and dataset and examining the results and comparing them against the research objectives and finding whihc algorithms yields the finest results by plotting the graphs for better understanding of the algorithms efficiency.
Online training of students of applied physics in the field of circuitry

Abstract The aim of the study is to test the effectiveness of online teaching of students of applied physics (directions “Electric power and electrical engineering”, “Physics”) to the basics of circuitry and robotics. For the practical development of the principles of circuitry, the TinkerCAD Circuits modeling system is used, lectures and consulting events are implemented using Skype online communication and YouTube broadcasts. The training is based on the principles of gradual complication of tasks and training in invention. The study was conducted at Sholom-Aleichem Priamursky State University and Osh State University in the 2020-2021 academic year. Two groups were selected for the study, the control group (60 students) studied the basics of circuitry using the prepared tasks, the experimental group (72 students) used the TinkerCAD modeling system for training. The results of the study showed the effectiveness of the methodology used for the assimilation of knowledge and skills: an increase in the control group was 13 percent, while in the experimental group an increase of 42 percent was noted. During the research, an increase in the interest of students in the performance of work, attempts to self-improve working devices was noted. Further improvement of the methodology may be associated with the technical design of remote laboratories for circuitry and robotics.
A Novel Fraud Detection Scheme for Credit Card Usage Employing Random Forest Algorithm Combined with Feedback Mechanism

As electronic commerce has gained widespread popularity, payments made for users' transactions through credit cards also gained an equal amount of reputation. Whenever shopping through the web is made, the chance for the occurrence of fraudulent activities are escalating. In this paper, we have proposed a three-phase scheme to detect fraudulent activities. A profile for the card users based on their behavior is created by employing a machine learning technique in the second phase extraction of a precise communicative pattern for the card users depending upon the accumulated transactions and the user's earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the fraudulent online activities that occurred. If an emerging transaction is fraudulent, feedback is taken, which resolves the drift's difficulty in the notion. Experiments performed indicated that the proposed scheme works better than other schemes.
Construction of innovative computer training education mode under the environment of multiple intelligences Internet of Things

With the emergence of the Internet of Things technology and the rapid development of computer technology and network communication technology, a profound technological change is taking place in the education model. Based on the current problems in the teaching of computer network courses and the improvement needs of the innovative computer training platform of the Internet of Multiple Intelligences, this article summarizes the ideas for improving the innovative computer training platform of the Internet of Multiple Intelligences of the computer network, and explores the mobile learning environment of computer network courses. Based on the multi-level feedback teaching mode, each type of experiment in the innovative computer training system is divided into theoretical tests, basic experiments, improved experiments and inquiry experiments. After each level is completed, the problem will be fed back to the teacher through the system, the results and suggestions will be fed back to the students, and the students will increase their interest in learning, improve the performance of innovative computer training, cultivate the students’ ability to explore and innovate, and help teachers to grasp the students’ innovation in computer training. This article demonstrates the teaching effect of the system through quasi-experimental research methods and interview methods. According to the results of quasi-experimental research and interviews, it is found that this system can improve the teaching effect of teachers, stimulate students’ interest in learning and improve academic performance. The construction and application research of this system will provide support for the construction and practical teaching of an innovative computer training system for the multi-dimensional Internet of Things.
Control Flow Integrity in IoT Devices with Performance Counters and DWT

IoT devices are open to traditional control flow integrity (CFI) attacks resulting from buffer overflow and return-oriented programming like techniques. They often have limited computational capacity ruling out many of the traditional heavy-duty software countermeasures. In this work, we deploy hardware/software solutions to detect CFI attacks. Some of the medium capability IoT devices, for example based on Raspberry Pi, contain ARM Cortex A-53 (Pi 3) or Cortex A-73 (Pi 4) processors. These processors include hardware counters to count microarchitecture level events affecting performance. Lighter weight IoT devices, say based on ARM Cortex M4 or M7, include DWT (Debug, Watch & Trace) module. When control flow anomalies caused by attacks such as buffer overflow or return oriented programming (ROP) occur, they leave a microarchitectural footprint. Hardware counters reflect such footprints to flag control flow anomalies. This paper is geared towards buffer overflow and ROP control flow anomaly detection in embedded programs. The targeted program entities are main event loops and task/event handlers. The proposed anomaly detection mechanism is evaluated on ArduPilot [1] - a popular autopilot software on a Raspberry Pi 3 with PMU and DWT. A self-navigation program is evaluated on an iCreate Roomba platform with an ARM Cortex M4 processor with DWT only. We are able to achieve 97-99%+ accuracy with 1-10 micro-second time overhead per control flow anomaly check.
Systematic transition from boundary extension to contraction along an object-scene continuum

After viewing a picture of a scene, our memory of it typically extends beyond what was presented—a phenomenon called boundary extension (Intraub et al, 1992). However, recent work has found evidence for boundary contraction as well (Bainbridge & Baker, 2020), proposing different outcomes for object-focused and scene-focused images. In naturalistic visual experience, there is a continuum of views that naturally transition between scene-focused and object-focused, e.g., as an observer enters an indoor environment and moves toward an object. Here, we examined how memory for a view of an environment changes along this object-scene continuum. We constructed 3D indoor environments using a virtual-reality game engine, allowing for tight control of their spatial dimensions and object content, and captured a series of images at systematic distances from a center object within each environment. We found continuous progression from boundary-extension in close-up views to boundary-contraction in distant views, with a consistent transition point across environments, and a clear relationship between the magnitude of the memory distortion and the position along the continuum. In a series of subsequent experiments, by manipulating the number of objects in the environment, we found that the memory transition point is not linked to a specific depicted distance per se, e.g., the transition between reachable and non-reachable space. Rather, the position at which boundary extension transitions to boundary contraction seems to track with the views that “looked best,” as judged by independent observers. Taken together, these results demonstrate that there is indeed a systematic link between the direction of memory distortion and the depicted scale of the space, but where object content has a clear influencing role. Further, the correspondence between the best view and the transition point suggests a common underlying mechanism, providing new empirical inroads for articulating the mechanisms of visual scene encoding.
Ensemble Three-Stream RGB-S Deep Neural Network for Human Behavior Recognition Under Intelligent Home Service Robot Environments

This paper presents a method for recognizing behaviors in videos based on the ensemble RGB-S deep neural network, which combines RGB images and skeleton features from an action recognition database built in intelligent home service robot environments. The ensemble model is designed using the three-stream approach. The first stream classifies behaviors in videos using a convolutional neural network (CNN) based on a pre-trained ResNet101 model, which uses two-dimensional (2D) sequence images of actions as its input, and training a long short-term memory (LSTM) neural network with the sequence (RGB 2D-CNN + LSTM). The second stream directly manages the video and uses a three-dimensional (3D) CNN to include both temporal and spatial information. The 3D CNN is based on a pre-trained R3D-18 model (RGB 3D-CNN). The last stream uses the pose evolution image (PEI) method, which converts the skeleton sequence into a single-color image. The converted images are used as the input for the CNN (Skeleton PEI-2D-CNN). This approach not only reflects the spatial and temporal features of the behaviors in videos, but also includes all characteristics of the 2D sequence images, 3D videos, and skeleton sequences. Finally, a large-scale database for behavior recognition in videos, known as ETRI-Activity3D, is used in this study to verify the performance of the proposed deep neural network. A recognition performance of 93.2% is achieved in a cross-subject experiment, verifying the superiority of this method over models from previous studies.