Reviewerly/exHarmony huggingface dataset:
GSANet: Semantic Segmentation With Global And Selective Attention

This paper proposes a novel deep learning architecture for semantic segmentation. The proposed Global and Selective Attention Network (GSANet) features Atrous Spatial Pyramid Pooling (ASPP) with a novel sparsemax global attention and a novel selective attention that deploys a condensation and diffusion mechanism to aggregate the multi-scale contextual information from the extracted deep features. A selective attention decoder is also proposed to process the GSA-ASPP outputs for optimizing the softmax volume. We are the first to benchmark the performance of semantic segmentation networks with the low-complexity feature extraction network (FXN) MobileNetEdge, that is optimized for low latency on edge devices. We show that GSANet can result in more accurate segmentation with MobileNetEdge, as well as with strong FXNs, such as Xception. GSANet improves the state-of-art semantic segmentation accuracy on both the ADE20k and the Cityscapes datasets.
Brainstorming for Sensemaking in a Multimodal, Multiuser Cognitive Environment

This paper discusses a user study conducted on a brainstorming tool for brainstorming in intelligence analysis, situated in a cognitive and immersive system. The cognitive and immersive room is comprised of gesture technology, voice input, tablet input, and a 360 display to allow for group discussion and interaction. The brainstorming tool design is informed by sensemaking theory as defined by Pirolli and Card []. The basis for the digital brainstorming tool in our cognitive immersive environment is a structured analytic tool already in use by the intelligence analysis domain. We anticipate that this design will capitalize on cognitive familiarity to enable a more approachable design and facilitate a smoother and more useful tool. The user study (n = 26) is an A/B study conducted to compare the capabilities of the analog brainstorming tool with the digital brainstorming tool, and understand how the multimodal interaction system impacts brainstorming.
Improving Discrete Latent Representations With Differentiable Approximation Bridges

Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters. In this work, we introduce a novel method to allow non-differentiable functions at intermediary layers of deep neural networks. We do so by training with a differentiable approximation bridge (DAB) neural network which approximates the non-differentiable forward function and provides gradient updates during backpropagation. We present empirical results from more than 600 experiments in four different domains: unsupervised (image) representation learning, variational (image) density estimation, image classification, and sequence sorting to demonstrate that our proposed method improves state of the art performance. We demonstrate that training with DAB aided discrete non-differentiable functions improves image reconstruction quality and posterior linear separability by 10% against the Gumbel-Softmax relaxed estimator [26], [37] as well as providing a 9% improvement in the test variational lower bound in comparison to the state of the art RELAX [16] discrete estimator. We also observe an accuracy improvement of 77% in neural sequence sorting and a 25% improvement against the straight-through estimator [5] in an image classification setting. The DAB network is not used for inference and expands the class of functions that are usable in neural networks. Our code is available at https://github.com/apple/ml-dab.
A Study on Behaviour Intention for using Chatbots

The present study uses the primary research to find out the behavior intention of an individual to use chatbots. The study has taken the five dimensions viz., reliability, responsiveness, assurance, empathy and tangibility and find out their association with behavior intention of an individual to use the chatbots. The sample of 270 respondents are taken to conclude the findings. It was concluded that out of these five dimensions, three dimensions are significantly associated with intention of using chatbots. These are reliability, empathy and tangibility.
You Only Need The Image: Unsupervised Few-Shot Semantic Segmentation With Co-Guidance Network

Few-shot semantic segmentation has recently attracted attention for its ability to segment unseen-class images with only a few annotated support samples. Yet existing methods not only need to be trained with a large scale of pixel-level annotations on certain seen classes, but also require a few annotated support image-mask pairs for the guidance of segmentation on each unseen class. In this paper, we propose the Co-guidance Network (CGNet) for unsupervised few-shot segmentation, which eliminates requirements of annotation on both seen and unseen classes. Specifically, CGNet segments unseen-class images with only unlabeled support images by the newly designed co-guidance mechanism. Moreover, CGNet is trained on seen classes by a novel co-existence recognition loss, which further removes the need of pixel-level annotations. Extensive experiments on the PASCAL -5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset show that the unsupervised CGNet performs comparably with the state-of-the-art fully-supervised few-shot methods, while largely alleviating annotation requirement.
Hardware Fingerprinting for the ARINC 429 Avionic Bus

ARINC 429 is the most common data bus in use today in civil avionics. Despite this, the protocol lacks any form of source authentication. A technician with physical access to the bus is able to replace a transmitter by a rogue device, and receivers will accept its malicious data as they have no method of verifying the authenticity of messages. Updating the protocol would close off security loopholes in new aircrafts but would require thousands of airplanes to be modified. An interim solution is required. We propose a hardware fingerprinting method for the ARINC 429 data bus, and analyze its performance in a sender authentication setting. Our approach relies on the observation that changes in hardware, such as replacing a transmitter or a receiver with a rogue one, modify the electric signal of the transmission. In this paper we explore the feasibility of designing an intrusion detection system based on hardware fingerprinting. Our analysis includes both a theoretical Markov-chain model and an extensive empirical evaluation. For this purpose, we collected a data corpus of ARINC 429 data traces, which may be of independent interest since, to the best of our knowledge, no public corpus is available. In our experiments, we show that it is feasible for an intrusion detection system to achieve a near-zero false alarms per second, while detecting a rogue transmitter in under 50 ms, and detecting a rogue receiver in under 3 s. This would allow a rogue component installed by a malicious technician to be detected during the pre-flight checks, well before the aircraft takes off. This is made possible due to the fact that we rely on the analog properties, and not on the digital content of the transmissions. Thus we are able to detect a hardware switch as soon as it occurs, even if the data that is being transmitted is completely normal.
Multi-level Feature Fusion Mechanism for Single Image Super-Resolution

Convolution neural network (CNN) has been widely used in Single Image Super Resolution (SISR). As the network deepens, the learning ability of the network becomes more and more powerful. However, most SISR methods based on CNN do not make full use of the hierarchical features and the learning ability of the network. These features cannot be extracted directly by subsequent layers, so the previous layer hierarchical information has little impact on the output and performance of subsequent layers relatively poor. To solve the above problem, a novel Multi-Level Feature Fusion network (MLRN) is proposed, which can make full use of global intermediate features. I also introduce Feature Skip Fusion Block (FSFblock) as a basic module. Each block can be extracted directly to the raw multi-scale feature and fusion multi-level feature, then learn spatial correlation. The correlation among the features of the holistic approach leads to a continuous global memory of information mechanism. Experiments on public datasets show that the method MLRN can be implemented, which has a favorable performance than most of the current methods.
Q-Learning of Spatial Actions for Hierarchical Planner of Cognitive Agents

In the paper, we consider the problem of the robotic movement inaccuracy. We suggest that clarifying the abstract actions of the behavior planner will help build more precise control of the robot. A multi-agent planner for the synthesis of the abstract actions with refinement for a two-dimensional movement task was proposed. We analyze the problem of the action execution by robots and present a way to solve navigation problems through the use of reinforcement learning and deep learning algorithms. This method made it possible to synthesize sets of atomic sub-actions for correcting the state of the robotic platform at each moment of time. We conducted a set of experiments in single and multiagent settings. The synthesis of sub-actions of several tasks formed a training set for the RL model, which was tested on a test example. All considered tasks consisted of moving robotic platforms across a map with obstacles and manipulating environmental objects.
Designing infographics/visual icons of social network by referencing to the design concept of ancient Oracle Bone characters

This paper introduces the use of pictogram design concept in ancient China for the development of a set of today's "graphic icons" or "infographics" in modern social network visualization systems. These graphic icons should be close to sensory symbols that derive their expressive power from their ability to use the perceptual processing power of the brain without learning. Therefore, with the use of such a set of "sensory symbols" we aim to achieve the identification of corresponding physical objects (or their attributes) to be performed close to the pre-attentive time.
Social Simulation with RBC

Social simulation is a cutting edge topic in an interdisciplinary field where Information Technology and Social Science overlap. In this paper, we set out the fundamental requirements for social simulation systems and demonstrate that Role-Based Collaboration (RBC) and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is highly qualified to meet these requirements.As a case study, peer review is a typical social process, a part of which can be modeled as a role assignment problem. After we present the basics of a current typical peer review process, we apply RBC and E-CARGO model to renovate the process. Simulation experiments show the benefits of this improvement.
Electric vehicle regional management system based on the BSP model and multi-information fusion

Aiming at the problem of most electric vehicles (EV) sounding false alarms due to touches, an EV alarm regionalization management control system based on multi-information fusion and the BSP model is designed in the present study. The proposed system uses multiple sensors to detect the state of the EV, and realizes multi-sensor information fusion by the Lagrange interpolation method. The ZigBee networking technology is used to carry out the regional management of EV alarms, establish a BSP model, realize the synchronous transmission of system status detection signals, and finally complete the alarm function. The experimental results show that the false alarm rate of traditional EV alarm systems is about 85.5%, that of alarm systems that perform state monitoring without information fusion is about 11.3%, and that of the proposed alarm system that performs multi-information fusion simultaneously is greatly reduced to about 0.5%.
Ensemble Feature Extraction-Based Detection of Abnormal Mass Present in Medical Images Using Machine Learning

Use of computer-aided diagnosis systems has been increasing in the medical domain due to the rising complexity and amount of medical data. Well-defined feature descriptors are must for computer-aided diagnostic systems. In this research paper, we attempt to create an ensembled feature extractor and selector for better classification of normal and abnormal medical images using different machine learning algorithms. In this research paper, two different data sets will be used one for oral cancer histopathology images and one for brain tumor MR images. The comparison of various feature extraction techniques will be done, and result analysis will also be provided at the end.
Area–Time-Efficient Code-Based Postquantum Key Encapsulation Mechanism on FPGA

Postquantum cryptography attracts a lot of attention from the research community recently due to the emergence threat from quantum computer toward the conventional cryptographic schemes. In view of that, NIST had initiated the standardization process in 2017. Bit flipping key encapsulation (BIKE) designed by Aragon et al. is one of the promising code-based schemes among the round-3 candidates. BIKE utilizes a quasi-cyclic medium density parity check (QC-MDPC) code and incorporates a few variants derived from the McEliece, Niederreiter, and Ouroboros schemes. In this article, we present efficient and constant time implementation of BIKEI and BIKE-III in field-programmable gate array (FPGA), which has the best area-time efficiency so far. We proposed modification to the original one-round bit flipping algorithm to achieve more area-time-efficient decoding in hardware, which achieved latency of 464.73 and 556.52 μs for BIKE-I and BIKE-III, respectively, in Virtex-7. A pipelined key encapsulation architecture is proposed to speedup the key encapsulation of BIKE-I and BIKE-III, achieving the latency of 146.47 and 153.25 μs on the same FPGA platform. Considering the Artix-7 FPGA platform, our combined key generation and encapsulation module for BIKE-I is also three more area-time efficient compared with the state-of-the-art BIKE-I implementation by Aragon et al.
Multispectral Domain Invariant Image for Retrieval-based Place Recognition

Multispectral recognition has attracted increasing attention from the research community due to its potential competence for many applications from day to night. However, due to the domain shift between RGB and thermal image, it has still many challenges to apply and to use RGB domain-based tasks. To reduce the domain gap, we propose multispectral domain invariant framework, which leverages the unpaired image translation method to generate a semantic and strongly discriminative invariant image by enforcing novel constraints in the objective function. We demonstrate the efficacy of the proposed method on mainly multispectral place recognition task and achieve significant improvement compared to previous works. Furthermore, we test on multispectral semantic segmentation and unsupervised domain adaptations to prove the scalability and generality of the proposed method. We will open our source code and dataset.
W-Com2Vec: A topic-driven meta-path- based intra-community embedding for content-based heterogeneous information network

Heterogeneous information network (HIN) are becoming popular across multiple applications in forms of complex large-scaled networked data such as social networks, bibliographic networks, biological networks, etc. Recently, information network embedding (INE) has aroused tremendously interests from researchers due to its effectiveness in information network analysis and mining tasks. From recent views of INE, community is considered as the mesoscopic preserving network’s structure which can be combined with traditional approach of network’s node proximities (microscopic structure preserving) to leverage the quality of network’s representation. Most of contemporary INE models, like as: HIN2Vec, Metapath2Vec, HINE, etc. mainly concentrate on microscopic network structure preserving and ignore the mesoscopic (intra-community) structure of HIN. In this paper, we introduce a novel approach of topic-driven meta-path-based embedding, namely W-Com2Vec (Weighted intra-community to vector). Our proposed W-Com2Vec model enables to capture richer semantic of node representation by applying the meta-path-based community-aware, node proximity preserving and topic similarity evaluation at the same time during the process of network embedding. We demonstrate comprehensive empirical studies on our proposed W-Com2Vec model with several real-world HINs. Experimental results show W-Com2Vec outperforms recent state-of-the-art INE models in solving primitive network analysis and mining tasks.
Comparative Effectiveness of Some Approaches to Extracting Most Informative Factors Influencing Algae Bioproductivity

Water eutrophication is a worldwide environmental problem. Understanding the mechanisms of the process help to prevent and remediate such eutrophication. The paper presents researches of the dependence of chlorophyll a concentration in phytoplankton on a number of different physicochemical factors on the basis of long-term observation data in the Kakhovka reservoir of Dnipro River. Four modeling methods were applied in this work: linear regression, two sorting-out GMDH algorithms (complete search combinatorial algorithm COMBI and directional search correlation-based sorting algorithm with regressors rating analysis CAR) and the LASSO. Main stress is laid on the possibility of automatic (more objective) discovering the most informative factors influencing algae bioproductivity.
Incidental Visualizations

In InfoVis design, visualizations make use of pre-attentive features to highlight visual artifacts and guide users' perception into relevant information during primitive visual tasks. These are supported by visual marks such as dots, lines, and areas. However, research assumes our pre-attentive processing only allows us to detect specific features in charts. We argue that a visualization can be completely perceived pre-attentively and still convey relevant information. In this work, by combining cognitive perception and psychophysics, we executed a user study with six primitive visual tasks to verify if they could be performed pre-attentively. The tasks were to find: horizontal and vertical positions, length and slope of lines, size of areas, and color luminance intensity. Users were presented with very simple visualizations, with one encoded value at a time, allowing us to assess the accuracy and response time. Our results showed that horizontal position identification is the most accurate and fastest task to do, and the color luminance intensity identification task is the worst. We believe our study is the first step into a fresh field called Incidental Visualizations, where visualizations are meant to be seen at-a-glance, and with little effort.
Lossless Compression Method for Rocket Telemetry Data based on Reorganizing Frame Information

Rocket launching is a high speed state of complex system. The rocket telemetry data shows values of working parameters and environmental parameters toward each system in the flight course. Rocket telemetry data with great variety and large quantity, can be compressed to improve communication efficiency and lower storage space requirement. By analyzing the frame format, correlation and statistical characteristics of rocket telemetry data, Lossless rocket telemetry data compression method is given based on undistorted information reorganization, in order to improve compression ratio. Experiments showed the feasibility of that method. Compare to general compression algorithms, the compression rate can be increased by 10-15%.
Block-Size Dependent Overlapped Block Motion Compensation

Overlapped block motion compensation (OBMC) is one of the inter prediction tools that improves coding performance. OBMC applied to various non-squared blocks has been studied in VVC, which is being standardized by joint video experts team (JVET), to improve coding performance over HEVC. Memory bandwidth, however, is a bottleneck when OBMC is used, and conventional methods have not achieved a good trade-off regarding coding performance and memory bandwidth so far. In this study, interpolation filters and applicable conditions of OBMC depending on block sizes are proposed to achieve the best trade-off. The experimental results show a -0.40% BD-rate gain compared with that of the VVC test model 3 for random access conditions under the common test condition in JVET.
Deep Neural Networks for Ortophoto-Based Vehicle Localization

Navigation of unmanned vehicle especially using orthophoto is a topic of active research. This paper is dedicated to study of different methods of orthophoto-based localization methods. For this task new dataset was created. It consists of pairs of ground level and bird’s eye view images collected on vehicle test site of the technology contest Up Great “Winter City”. Different deep network approaches to localization were used: 1) embedding-based, 2) based on synthesis of bird’s eye view using Pix2pix conditional generative adversarial network and masked cross-correlation in map subwindow. The second approach has demonstrated good applicability for the proposed dataset. Mean absolute error of localization on known scenes reached 1 m. The average total time of bird’s eye view generation and subsequent localization is from 0.1 s to 0.2 s. This is an acceptable quality for the task solution and its further use as part of the navigation systems of unmanned vehicles.
Recommending Restaurants: A Collaborative Filtering Approach

Recommender systems are algorithms for suggesting relevant items to users (items being movies, books, products to buy or anything else depending on industries). By building up a Recommender System which could assist a client with deciding which restaurant one should visit. There are different factors depending on which a user settles on a choice of visiting a restaurant like the sort of food of the restaurant, the area of the restaurant, the climate, approximate cost, reputation, ratings, and so forth. So as to discover a descent machine learning model, we have attempted various collaborative filtering models to predict the ratings between restaurants and users. The algorithms we have implemented are the k-Nearest Neighbors algorithm and the multiclass SVM classification. Our assessment shows that the multiclass SVM classification method shows the best result. For rating prediction, we correlate user-based and item-based collaborative filtering methods.
Capsule Deep Generative Model That Forms Parse Trees

Supervised capsule networks are theoretically advantageous over convolutional neural networks, because they aim to model a range of transformations of local physical or abstract objects and part-whole relationships among them. However, it remains unclear how to use the concept of capsules in deep generative models. In this study, to address this challenge, we present a statistical modelling of capsules in deep generative models where distributions are formulated in the exponential family. The major contribution of this unsupervised method is that parse trees as representations of part-whole relationships can be dynamically learned from the data.