Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Accurate Binary Neural Networks from Scratch

Binary neural networks are a promising approach to execute convolutional neural networks on devices with low computational power. Previous work on this subject often quantizes pretrained full-precision models and uses complex training strategies. In our work, we focus on increasing the performance of binary neural networks by training from scratch with a simple training strategy. In our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. Further, we analyze how full-precision network structures can be adapted for efficient binary networks and adopt a network architecture based on a DenseNet for binary networks, which lets us improve the state-of-the-art even further. Our source code can be found online: https://github.com/hpi-xnor/BMXNet-v2
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network

User and item representation learning is critical for recommendation. Many of existing recommendation methods learn representations of users and items based on their ratings and reviews. However, the user-user and item-item relatedness are usually not considered in these methods, which may be insufficient. In this paper, we propose a neural recommendation approach which can utilize useful information from both review content and user-item graphs. Since reviews and graphs have different characteristics, we propose to use a multi-view learning framework to incorporate them as different views. In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews. In addition, we propose to incorporate a three-level attention network into this view to select important words, sentences and reviews for learning informative user and item representations. In the graph-view, we propose a hierarchical graph neural network to jointly model the user-item, user-user and item-item relatedness by capturing the first- and second-order interactions between users and items in the user-item graph. In addition, we apply attention mechanism to model the importance of these interactions to learn informative user and item representations. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification

Recent work has shown that current text classification models are fragile and sensitive to simple perturbations. In this work, we propose a novel adversarial training approach, LexicalAT, to improve the robustness of current classification models. The proposed approach consists of a generator and a classifier. The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks. Considering the diversity of attacks, the generator uses a large-scale lexical knowledge base, WordNet, to generate attacking examples by replacing some words in training examples with their synonyms (e.g., sad and unhappy), neighbor words (e.g., fox and wolf), or super-superior words (e.g., chair and armchair). Due to the discrete generation step in the generator, we use policy gradient, a reinforcement learning approach, to train the two modules. Experiments show LexicalAT outperforms strong baselines and reduces test errors on various neural networks, including CNN, RNN, and BERT.
Development of Interest and Role of Choice During Sequential Knowledge Acquisition

Interest is an important motivational element for learning in the school environment. However, little research has directly addressed how interest changes over time as knowledge accumulates. To gain a better understanding of how knowledge acquisition influences intraindividual change of interest, we developed a novel paradigm in which participants gain step-by-step information about lesser known countries. After reading each piece of information, participants rated their interest in the country. Growth-curve modelling showed that interest grows during knowledge acquisition until it eventually stalls and starts to decline. We also found that the opportunity to choose information boosted the growth in interest and delayed its decline. Further analysis revealed that people disengaged from a topic (i.e., stopped accessing information about a particular country) when their interest started to decrease.
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks

Aspect-level sentiment classification, which is a fine-grained sentiment analysis task, has received lots of attention these years. There is a phenomenon that people express both positive and negative sentiments towards an aspect at the same time. Such opinions with conflicting sentiments, however, are ignored by existing studies, which design models based on the absence of them. We argue that the exclusion of conflict opinions is problematic, for the reason that it represents an important style of human thinking – dialectic thinking. If a real-world sentiment classification system ignores the existence of conflict opinions when it is designed, it will incorrectly mixed conflict opinions into other sentiment polarity categories in action. Existing models have problems when recognizing conflicting opinions, such as data sparsity. In this paper, we propose a multi-label classification model with dual attention mechanism to address these problems.
IMPROVING EFFICIENCY OF AWEB-BASEDDISTRIBUTED EVOLUTIONARY COMPUTING SYSTEM

The paper presents a study on performance improvement of an evolutionary computing system built on the basis of JavaScript technology. The system consists of web browsers performing computational tasks and a web server based on Go programming language (Golang technology) that controls the computing environment. The newest web technologies such as HTML5 Web Workres and JavaScript WebAssembly are used to maximize the utilization of the resources provided by web clients. Computational experiments are performed on large instances of popular NP-hard problems: travelling salesman problem (TSP), flowshop scheduling problem (FSP) and recourse constrained project scheduling problem (RCPSP).
Pathway to presence: an investigation of ambient awareness in online learning environments

ABSTRACT Due to the pervasiveness of online technology, especially social media and social networking sites (SNS) like Twitter and Facebook were are surrounded by a constant stream of information about other individuals. On the other hand, even though distance and online learning has become mainstream, there are still concerns leveled at many of these educational offerings, one of them being the relative social isolation of online and distance students. In this study, we investigate if and how ambient awareness, a concept originated in research in interpersonal effects of SNS, can play an important role in how salient social impressions of peers emerge and how this leads to perceptions of social presence among students. To this end, we use an impression formation paradigm in a four-week online distance course (N = 51). We found that students were able to form impressions of their peers through ambient awareness. These impressions differed in prevalence from the ones gathered through direct social interaction. In a larger second sample (N = 169), we find ambient awareness to be a mediator between the sociability of the learning environment and perceptions of social presence. Implications, limitations and suggestions for further research are discussed.
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several subtasks and then solve them sequentially, which leads to following issues: 1) errors in earlier subtasks will be propagated and negatively affect downstream ones; and 2) each subtask cannot naturally share supervision signals with others. To tackle these issues, we propose an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model. The proposed framework thus enables shared supervisions and alleviates the effect of error propagation. Experiments on a large-scale conversational question answering dataset containing 1.6M question answering pairs over 12.8M entities show that the proposed framework improves overall F1 score from 67% to 79% compared with previous state-of-the-art work.
Computational Analysis and Design Optimization of Convective PCR Devices

COMPUTATIONAL ANALYSIS AND DESIGN OPTIMIZATION OF CONVECTIVE PCR DEVICES Jung Il Shu Old Dominion University, 2019 Director: Dr. Oktay Baysal Polymerase Chain Reaction (PCR) is a relatively novel technique to amplify a few copies of DNA to a detectable level. PCR has already become common in biomedical research, criminal forensics, molecular archaeology, and so on. Many have attempted to develop PCR devices in numerous types for the purpose of the lab-on-chip (LOC) or point-of-care (POC). To use PCR devices for POC lab testing, the price must be lower, and the performance should be comparable to the lab devices. For current practices with the existing methods, the price is pushed up higher partially due to too much dependence on numerous developmental experiments. Our proposition herein is that the computational methods can make it possible to design the device at lower cost and less time, and even improved performance. In the present dissertation, a convective PCR, that is the required flow circulation is driven by the buoyancy forces, is researched towards the use in POC testing. Computational Fluid Dynamics (CFD) is employed to solve the nonlinear equations for the conjugate momentum and heat transfer model and the species transport model. The first application of the models considers four reactors in contact with two separate heaters, but with different heights. Computational analyses are carried out to study the nature of buoyancy-driven flow for DNA amplification and the effect of the capillary heights on the performance. The reactor performance is quantified by the doubling time of DNA and the results are experimentally verified. The second application includes a novel design wherein a reactor is heated up by a single heater. A process is established for low-developmental cost and high-performance design. The best is searched for and found by evaluating the performance for all possible candidates. The third application focuses on the analysis of the performance of single-heater reactors affected by positions of a capillary tube: (1) horizontal, and (2) vertical. In the last application, numerous double-heater reactor designs are considered to find the one that assure the optimal performance. Artificial Neural Network (ANN) is employed to approximate the CFD results for optimization. In summary, through the four segments of our studies, the results show significant possibilities of increasing the performance and reducing the developmental cost and time. It is also demonstrated that the proposed methodology is advantageous for the development of cPCR reactors for the purpose of POC applications.
Convergence of Adversarial Training in Overparametrized Neural Networks

Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial training, a heuristic form of robust optimization that alternates between minimization and maximization steps, has proven to be among the most successful methods to train networks to be robust against a pre-defined family of perturbations. This paper provides a partial answer to the success of adversarial training, by showing that it converges to a network where the surrogate loss with respect to the the attack algorithm is within $\epsilon$ of the optimal robust loss. Then we show that the optimal robust loss is also close to zero, hence adversarial training finds a robust classifier. The analysis technique leverages recent work on the analysis of neural networks via Neural Tangent Kernel (NTK), combined with motivation from online-learning when the maximization is solved by a heuristic, and the expressiveness of the NTK kernel in the $\ell_\infty$-norm. In addition, we also prove that robust interpolation requires more model capacity, supporting the evidence that adversarial training requires wider networks.
Phylogenetic methods applied to extinct beetles — the case of †Tunguskagyrus (Gyrinidae or †Triaplidae)

†Tunguskagyrus, a Late Permian beetle fossil, was assigned to the adephagan family Gyrinidae in the original description. This placement was challenged in a recent study, where the genus was considered as part of a broad new concept of the extinct family †Triaplidae and transferred from Adephaga to Archostemata. Even though some structural features deviate from the typical pattern found in extant Gyrinidae, we maintain the placement of †Tunguskagyrus in the stemgroup of this family as the most plausibe interpretation. The suggested taxonomic changes concerning †Triaplidae (and †Schizophoroidea) are invalidated by an insufficient phylogenetic approach and several misinterpretations of the fossil imprints. The arguments are based on 1) general habitual similarity, 2) ubiquitous characters, 3) plesiomorphies, or 4) apomorphic conditions (compared to the Coleopteran groundplan) occurring in many beetle groups, strongly affected by homoplasy. The taxonomic shift of †Tunguskagyrus and the new concept of †Schizophoroidea and †Triaplidae are discarded due to the lack of a defined phylogenetic methodology and synapomorphies.
How can WeChat contribute to psychosocial benefits? Unpacking mechanisms underlying network size, social capital and life satisfaction among sojourners

Purpose The purpose of this paper is to systematically unpack whether and how WeChat network size is correlated with perceived social capital and life satisfaction among Chinese overseas students in Germany. Design/methodology/approach Based on a web-based survey data collected from 218 sojourners, zero-order correlation analyses and structural equation modeling were separately implemented to tackle the complicated research questions. Findings Findings demonstrate that an individual’s WeChat network size significantly and directly influences bridging, bonding and maintained social capital. Importantly, a path model demonstrates that these distinct dimensions of social capital are all significant predictors of life satisfaction. Furthermore, the empirical evidence reveals that bridging, bonding and maintained social capital can mediate the association between network size and well-being outcome. Research limitations/implications Theoretically, the paper is an initial attempt contributing to the previous studies on estimating the influence of WeChat friendships on social capital and life satisfaction. Practically, these findings will contribute to a more nuanced understanding of effect of personal network characteristics derived from the dominant media interaction on social connectedness and life quality. Originality/value Despite WeChat is immensely prevalent in Mainland China, only few studies have concentrated on the role of WeChat network size in fostering individuals’ social and psychological development. The work provides unique evidence that number of friends on the emerging technology could indirectly benefit sojourners’ satisfaction with life through processes involving diverse categories of social capital in a trans-cultural environment.
Geometry-Aware Neural Rendering

Understanding the 3-dimensional structure of the world is a core challenge in computer vision and robotics. Neural rendering approaches learn an implicit 3D model by predicting what a camera would see from an arbitrary viewpoint. We extend existing neural rendering to more complex, higher dimensional scenes than previously possible. We propose Epipolar Cross Attention (ECA), an attention mechanism that leverages the geometry of the scene to perform efficient non-local operations, requiring only $O(n)$ comparisons per spatial dimension instead of $O(n^2)$. We introduce three new simulated datasets inspired by real-world robotics and demonstrate that ECA significantly improves the quantitative and qualitative performance of Generative Query Networks (GQN).
Adaptive GNN for Image Analysis and Editing

Graph neural network (GNN) has powerful representation ability, but optimal configurations of GNN are non-trivial to obtain due to diversity of graph structure and cascaded nonlinearities. This paper aims to understand some properties of GNN from a computer vision (CV) perspective. In mathematical analysis, we propose an adaptive GNN model by recursive definition, and derive its relation with two basic operations in CV: filtering and propagation operations. The proposed GNN model is formulated as a label propagation system with guided map, graph Laplacian and node weight. It reveals that 1) the guided map and node weight determine whether a GNN leads to filtering or propagation diffusion, and 2) the kernel of graph Laplacian controls diffusion pattern. In practical verification, we design a new regularization structure with guided feature to produce GNN-based filtering and propagation diffusion to tackle the ill-posed inverse problems of quotient image analysis (QIA), which recovers the reflectance ratio as a signature for image analysis or adjustment. A flexible QIA-GNN framework is constructed to achieve various image-based editing tasks, like face illumination synthesis and low-light image enhancement. Experiments show the effectiveness of the QIA-GNN, and provide new insights of GNN for image analysis and editing.
Exploiting Monolingual Data at Scale for Neural Machine Translation

While target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated. In this work, we study how to use both the source-side and target-side monolingual data for NMT, and propose an effective strategy leveraging both of them. First, we generate synthetic bitext by translating monolingual data from the two domains into the other domain using the models pretrained on genuine bitext. Next, a model is trained on a noised version of the concatenated synthetic bitext where each source sequence is randomly corrupted. Finally, the model is fine-tuned on the genuine bitext and a clean version of a subset of the synthetic bitext without adding any noise. Our approach achieves state-of-the-art results on WMT16, WMT17, WMT18 English\leftrightarrowGerman translations and WMT19 German\toFrench translations, which demonstrate the effectiveness of our method. We also conduct a comprehensive study on how each part in the pipeline works.
Same-Cluster Querying for Overlapping Clusters

Overlapping clusters are common in models of many practical data-segmentation applications. Suppose we are given $n$ elements to be clustered into $k$ possibly overlapping clusters, and an oracle that can interactively answer queries of the form ``do elements $u$ and $v$ belong to the same cluster? '' The goal is to recover the clusters with minimum number of such queries. This problem has been of recent interest for the case of disjoint clusters. In this paper, we look at the more practical scenario of overlapping clusters, and provide upper bounds (with algorithms) on the sufficient number of queries. We provide algorithmic results under both arbitrary (worst-case) and statistical modeling assumptions. Our algorithms are parameter free, efficient, and work in the presence of random noise. We also derive information-theoretic lower bounds on the number of queries needed, proving that our algorithms are order optimal. Finally, we test our algorithms over both synthetic and real-world data, showing their practicality and effectiveness.
Cryptography and Network Security - Principles and Practice

solution manual which you'll search & downloads manuals from the premier AND NETWORK SECURITY PRINCIPLES AND PRACTICE 5TH EDITION. Free Essays on Solutions Manual Cryptography And Network Security Principles And Practice 5Th Edition for students. Use our papers to help you with yours. CRYPTOGRAPHY AND NETWORK SECURITY PRINCIPLES AND PRACTICE FIFTH EDITION William Stallings Prentice Hall Boston Columbus Indianapolis.
Visual Concept-Metaconcept Learning

Humans reason with concepts and metaconcepts: we recognize red and blue from visual input; we also understand that they are colors, i.e., red is an instance of color. In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associated question-answer pairs. The key is to exploit the bidirectional connection between visual concepts and metaconcepts. Visual representations provide grounding cues for predicting relations between unseen pairs of concepts. Knowing that red and blue are instances of color, we generalize to the fact that green is also an instance of color since they all categorize the hue of objects. Meanwhile, knowledge about metaconcepts empowers visual concept learning from limited, noisy, and even biased data. From just a few examples of purple cubes we can understand a new color purple, which resembles the hue of the cubes instead of the shape of them. Evaluation on both synthetic and real-world datasets validates our claims.
A Pilot Study for Chinese SQL Semantic Parsing

The task of semantic parsing is highly useful for dialogue and question answering systems. Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides cross-domain samples with multiple tables and complex queries. We build a Spider dataset for Chinese, which is currently a low-resource language in this task area. Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English. We compare character- and word-based encoders for a semantic parser, and different embedding schemes. Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.
Real-Time Tracking of Corneal Contour in Dalk Surgical Navigation Using Deep Neural Networks

Corneal disease is one of the most common causes of blindness for human beings in the world. Deep anterior lamellar k-eratoplasty (DALK) is a widely-used corneal transplantation technique, which requires precise control of surgical tools. This paper proposes a deep learning framework of augmented reality (AR) based surgical navigation to guide the suturing process in DALK. It aims to track the cutting corneal contour robustly through semantic segmentation and occlusion reconstruction. We devise a novel optical flow inpainting network to restore the missing motion caused by occlusion. The occluded regions are obtained using weakly-supervised segmentation of surgical tools and reconstructed by the key-frame warping along the completed optical flow. We introduce two kinds of loss functions to adapt the inpainting network to the optical flow space. The performance of our techniques is evaluated using real surgery videos from Shandong Eye Hospital. All experimental results show that our approach can achieve accurate corneal contour tracking subject to complex disturbance of tools in real-time surgical scenarios.