Reviewerly/exHarmony huggingface dataset:
Digital University Admission Application System with Study Documents Using Smart Contracts on Blockchain

In order to promote data distribution and utilization of personal data, the concept of data portability attracts attention. As an example of such personal data, there is a study document of a high school student used for entrance examination etc. Since study documents are still paper medium in Japan, computerization of study documents is planned. However, since a study document contains a lot of confidential information, the information must not be leaked even if erroneous transmission or mistake occurs. In addition, it is necessary to prevent falsification of information. The format of a study document is now planned to be updated by Ministry of Education, Culture, Sports, Science and Technology, and, in a updated study document, not only teachers input information but also student him/herself also conducts research activities, extra-curricular activities, qualifications/certification etc. Consequently, the authority of input/viewing becomes extremely complicated. In this paper, we propose a digital university admission application system with study documents and e-portfolio using smart contracts on blockchain. Furthermore, we implement the proposed system using Ethereum platform. Due to the property of the blockchain, the falsification of information is extremely difficult, information leakage is prevented by combining with encryption, and it is effective from the viewpoint of data portability.
Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks

In the given article, we present a novel approach in the paralinguistic field of age and gender recognition by speaker voice based on deep neural networks. The training and testing of proposed models were implemented on the German speech corpus aGender. We conducted experiments using different network topologies, including neural networks with fully-connected and convolutional layers. In a joint recognition of speaker age and gender, our system reached the recognition performance measured as unweighted accuracy of 48.41%. In a separate age and gender recognition setup, the obtained performance was 57.53% and 88.80%, respectively. Applied deep neural networks provide the best result of speaker age recognition in comparison to existing traditional classification methods.
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

Neural networks models for NLP are typically implemented without the explicit encoding of language rules and yet they are able to break one performance record after another. This has generated a lot of research interest in interpreting the representations learned by these networks. We propose here a novel interpretation approach that relies on the only processing system we have that does understand language: the human brain. We use brain imaging recordings of subjects reading complex natural text to interpret word and sequence embeddings from 4 recent NLP models - ELMo, USE, BERT and Transformer-XL. We study how their representations differ across layer depth, context length, and attention type. Our results reveal differences in the context-related representations across these models. Further, in the transformer models, we find an interaction between layer depth and context length, and between layer depth and attention type. We finally hypothesize that altering BERT to better align with brain recordings would enable it to also better understand language. Probing the altered BERT using syntactic NLP tasks reveals that the model with increased brain-alignment outperforms the original model. Cognitive neuroscientists have already begun using NLP networks to study the brain, and this work closes the loop to allow the interaction between NLP and cognitive neuroscience to be a true cross-pollination.
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition

We study the problem of top-k selection over a large domain universe subject to user-level differential privacy. Typically, the exponential mechanism or report noisy max are the algorithms used to solve this problem. However, these algorithms require querying the database for the count of each domain element. We focus on the setting where the data domain is unknown, which is different than the setting of frequent itemsets where an apriori type algorithm can help prune the space of domain elements to query. We design algorithms that ensures (approximate) differential privacy and only needs access to the true top-k' elements from the data for any chosen k' ≥ k. This is a highly desirable feature for making differential privacy practical, since the algorithms require no knowledge of the domain. We consider both the setting where a user's data can modify an arbitrary number of counts by at most 1, i.e. unrestricted sensitivity, and the setting where a user's data can modify at most some small, fixed number of counts by at most 1, i.e. restricted sensitivity. Additionally, we provide a pay-what-you-get privacy composition bound for our algorithms. That is, our algorithms might return fewer than k elements when the top-k elements are queried, but the overall privacy budget only decreases by the size of the outcome set.
Zero-shot Learning via Simultaneous Generating and Learning

To overcome the absence of training data for unseen classes, conventional zero-shot learning approaches mainly train their model on seen datapoints and leverage the semantic descriptions for both seen and unseen classes. Beyond exploiting relations between classes of seen and unseen, we present a deep generative model to provide the model with experience about both seen and unseen classes. Based on the variational auto-encoder with class-specific multi-modal prior, the proposed method learns the conditional distribution of seen and unseen classes. In order to circumvent the need for samples of unseen classes, we treat the non-existing data as missing examples. That is, our network aims to find optimal unseen datapoints and model parameters, by iteratively following the generating and learning strategy. Since we obtain the conditional generative model for both seen and unseen classes, classification as well as generation can be performed directly without any off-the-shell classifiers. In experimental results, we demonstrate that the proposed generating and learning strategy makes the model achieve the outperforming results compared to that trained only on the seen classes, and also to the several state-of-the-art methods.
MetaInit: Initializing learning by learning to initialize

Deep learning models frequently trade handcrafted features for deep features learned with much less human intervention using gradient descent. While this paradigm has been enormously successful, deep networks are often difficult to train and performance can depend crucially on the initial choice of parameters. In this work, we introduce an algorithm called MetaInit as a step towards automating the search for good initializations using meta-learning. Our approach is based on a hypothesis that good initializations make gradient descent easier by starting in regions that look locally linear with minimal second order effects. We formalize this notion via a quantity that we call the gradient quotient, which can be computed with any architecture or dataset. MetaInit minimizes this quantity efficiently by using gradient descent to tune the norms of the initial weight matrices. We conduct experiments on plain and residual networks and show that the algorithm can automatically recover from a class of bad initializations. MetaInit allows us to train networks and achieve performance competitive with the state-of-the-art without batch normalization or residual connections. In particular, we find that this approach outperforms normalization for networks without skip connections on CIFAR-10 and can scale to Resnet-50 models on Imagenet.
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs

The exploration bonus is an effective approach to manage the exploration-exploitation trade-off in Markov Decision Processes (MDPs). While it has been analyzed in infinite-horizon discounted and finite-horizon problems, we focus on designing and analysing the exploration bonus in the more challenging infinite-horizon undiscounted setting. We first introduce SCAL+, a variant of SCAL (Fruit et al. 2018), that uses a suitable exploration bonus to solve any discrete unknown weakly-communicating MDP for which an upper bound $c$ on the span of the optimal bias function is known. We prove that SCAL+ enjoys the same regret guarantees as SCAL, which relies on the less efficient extended value iteration approach. Furthermore, we leverage the flexibility provided by the exploration bonus scheme to generalize SCAL+ to smooth MDPs with continuous state space and discrete actions. We show that the resulting algorithm (SCCAL+) achieves the same regret bound as UCCRL (Ortner and Ryabko, 2012) while being the first implementable algorithm for this setting.
Semi-Implicit Graph Variational Auto-Encoders

Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierarchical variational framework to enable neighboring node sharing for better generative modeling of graph dependency structure, together with a Bernoulli-Poisson link decoder. Not only does this hierarchical construction provide a more flexible generative graph model to better capture real-world graph properties, but also does SIG-VAE naturally lead to semi-implicit hierarchical variational inference that allows faithful modeling of implicit posteriors of given graph data, which may exhibit heavy tails, multiple modes, skewness, and rich dependency structures. SIG-VAE integrates a carefully designed generative model, well suited to model real-world sparse graphs, and a sophisticated variational inference network, which propagates the graph structural information and distribution uncertainty to capture complex posteriors. SIG-VAE clearly outperforms a simple combination of VGAE with variational inference, including semi-implicit variational inference~(SIVI) or normalizing flow (NF), which does not propagate uncertainty in its inference network, and provides more interpretable latent representations than VGAE does. Extensive experiments with a variety of graph data show that SIG-VAE significantly outperforms state-of-the-art methods on several different graph analytic tasks.
Discriminator optimal transport

Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution $p$ and the generator distribution $p_G$. It implies that the trained discriminator can approximate optimal transport (OT) from $p_G$ to $p$. Based on some experiments and a bit of OT theory, we propose discriminator optimal transport (DOT) scheme to improve generated images. We show that it improves inception score and FID calculated by un-conditional GAN trained by CIFAR-10, STL-10 and a public pre-trained model of conditional GAN trained by ImageNet.
Feature Selection for Job Matching Application using Profile Matching Model

The advancement of technology has created big changes to speed up the job hiring process nowadays. With more tools developed, companies have embraced tools that help them recruits' talents. This is a research-in-progress of developing a job matching application system for Job Recruitment Agency specifically in the province of Pangasinan, Philippines. This paper proposes an approach for the agency to extract the relevant information from resumes and analyze it based on the different attributes. With the identification of the attributes, the proposed system is directed to adopt a clustering algorithm to match the profile of the job seekers against the requirements of the job posted by the prospect employers. This helps the agency to find suitable candidates for a particular job in different companies and make more informed decisions.
Determination of a class of permutation trinomials in characteristic three

Let f(X)=X(1+aXq(q−1)+bX2(q−1))∈Fq2[X], where a,b∈Fq2⁎. In a series of recent papers by several authors, sufficient conditions on a and b were found for f to be a permutation polynomial (PP) of Fq2 and, in characteristic 2, the sufficient conditions were shown to be necessary. In the present paper, we confirm that in characteristic 3, the sufficient conditions are also necessary. More precisely, we show that when charFq=3, f is a PP of Fq2 if and only if (ab)q=a(bq+1−aq+1) and 1−(b/a)q+1 is a square in Fq⁎.
Modulation Identification of Underwater Acoustic Communications Signals Based on Generative Adversarial Networks

The modern military needs for information acquisition and processing make the modulation identification of underwater acoustic communication signals become the focus of research. We propose a modulation identification method based on generative adversarial networks (GAN) to increase the robustness of modulation identification for underwater acoustic communication signals. The generator of GAN is trained to enhance the distorted signals and the discriminator is trained to extract features from underwater acoustic communication signals and classify them automatically. This method relies less expertise in signal processing. Simulating experiments are performed to evaluate the performance of the proposed method under multipath fading and additive white gaussian noise (AWGN) channel, and the result shows that the proposed method reaches higher accuracy than using a deep convolution neural network.
Enhanced Transfer Learning with ImageNet Trained Classification Layer

Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prior research has shown that this approach is capable of improving task performance. However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be frozen for optimal performance. Our empirical analysis demonstrates that the proposed fine-tuning performs better than traditional fine-tuning. This finding indicates that the pre-trained classification layer holds less category-specific or more global information than believed earlier. Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task. Our study manifests that careful normalization and scaling are essential for creating harmony between the pre-trained and new layers for target domain adaptation. We evaluate the proposed depth augmented networks for fine-tuning on several challenging benchmark datasets and show that they can achieve higher classification accuracy than contemporary transfer learning approaches.
Physics informed topology learning in networks of linear dynamical systems

Learning influence pathways in a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that includes physical systems with flow conservation, the topology of the interactions can be exactly recovered, even for colored exogenous inputs. The efficacy of the approach is illustrated through simulation and experiments on multiple important networks, including consensus networks, IEEE power networks and EnergyPlus based simulation of thermal dynamics of buildings.
D2NN

Deep Neural Networks (DNNs) have attracted mainstream adoption in various application domains. Their reliability and security are therefore serious concerns in those safety-critical applications such as surveillance and medical systems. In this paper, we propose a novel dual modular redundancy framework for DNNs, namely D2NN, which is able to tradeoff the system robustness with overhead in a fine-grained manner. We evaluate D2NN framework with DNN models trained on MNIST and CIFAR10 datasets under fault injection attacks, and experimental results demonstrate the efficacy of our proposed solution.
Leakage Resilience of the Duplex Construction

Side-channel attacks, especially differential power analysis (DPA), pose a serious threat to cryptographic implementations deployed in a malicious environment. One way to counter side-channel attacks is to design cryptographic schemes to withstand them, an area that is covered amongst others by leakage resilient cryptography. So far, however, leakage resilient cryptography has predominantly focused on block cipher based designs, and insights in permutation based leakage resilient cryptography are scarce. In this work, we consider leakage resilience of the keyed duplex construction: we present a model for leakage resilient duplexing, derive a fine-grained bound on the security of the keyed duplex in said model, and map it to ideas of Taha and Schaumont (HOST 2014) and Dobraunig et al. (ToSC 2017) in order to use the duplex in a leakage resilient manner.
Convolution Based Feature Extraction for Edge Computing Access Authentication

In this article, a convolutional neural network (CNN) enhanced radio frequency fingerprinting (RFF) authentication scheme is presented for Internet of things (IoT). RFF is a non-cryptographic authentication technology, identifies devices through the waveforms of the RF transient signals by processing received RF signals on the edge server, which places no cost burden to low-end (low-cost) devices without implementing any encryption algorithm and meet the demands of the real-time access authentication in Internet of things. In the new scheme, the feasibility of extracting features based on one-dimensional (1D) signal convolution is discussed, referring to the method of extracting features from CNN, and combining with the characteristics of signal convolution. A convolution kernel for 1D signals is designed to extract the feature of signals in order to reduce training time and ensure classification accuracy. Therefore, it can improve the accuracy compared with these traditional algorithms, while saving the training time of updating parameters repeatedly as the neural network. The accuracy and training time of thealgorithm are verified in a real signal acquisition system. The results prove that the novel algorithm can effectively improve the classification accuracy in low signal-to-noise ratio (SNR), while keeps the training time in an acceptable range.
Multiple people tracking with articulation detection and stitching strategy

Multiple people tracking in a monocular video of crowded scenes is a challenging problem, methods of which are mostly based on tracking-by-detection strategies. The result of detection preprocessing used by many tracking methods to avoid creating wrong targets, is likely to be contaminated when there are defective detections in datasets of benchmark. We propose an articulation-based detection selecting method to screen out detections unqualified for further processing. For the association part of tracking workflow, applying minimax operation can minimize the max intra-distance but results in discontinuous trajectories. We design a stitching strategy to link the tracklets created by minimax algorithm. The experimental results will demonstrate that the proposed method outperforms or is comparable to previous approaches.
Explanation by Progressive Exaggeration

As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (eg saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually change the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a ``tuning knob'' to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.
Accelerator Implementation of Lenet-5 Convolution Neural Network Based on FPGA with HLS

Convolution neural network is widely used in image recognition because it can imitate the behavioral characteristics of biological visual nerve, and has high recognition accuracy. It is a kind of feed forward neural network which contains convolution computation and has deep structure. Also it is one of the representative algorithms of deep learning. Because the convolution neural network has a special calculation mode, the general processor is not high in the implementation efficiency of the convolution neural network and can not meet the performance requirements. In order to solve this problem, we implement convolution neural network on FPGA and optimize convolution operation to improve computing parallelism, data throughput and energy efficiency of traditional processors. Finally, we implemented the convolution neural network of the Lenet-5 model on the ZYBOZ7 FPGA board and compared it with traditional processor. We realized the fast recognition of a picture at the frequency of 100M Hz with DMA control. The data throughput of FPGA is more than four times higher than that of general processor., and the power consumption is 1.8W, which is much lower than that of general processor.
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.

\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. This paper proposes DropEdge, a novel and flexible technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer. Furthermore, we theoretically demonstrate that DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by it. More importantly, our DropEdge is a general skill that can be equipped with many other backbone models (e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance. Extensive experiments on several benchmarks verify that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs. The effect of DropEdge on preventing over-smoothing is empirically visualized and validated as well. Codes are released on~\url{this https URL}.