Reviewerly/exHarmony huggingface dataset:
Information Obfuscation of Graph Neural Networks

While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes. In this paper, we study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance. Our method creates a strong defense against inference attacks, while only suffering small loss in task performance. Theoretically, we analyze the effectiveness of our framework against a worst-case adversary, and characterize an inherent trade-off between maximizing predictive accuracy and minimizing information leakage. Experiments across multiple datasets from recommender systems, knowledge graphs and quantum chemistry demonstrate that the proposed approach provides a robust defense across various graph structures and tasks, while producing competitive GNN encoders for downstream tasks.
Image Deformation Estimation via Multiobjective Optimization

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.
Achieving Lightweight Privacy-Preserving Image Sharing and Illegal Distributor Detection in Social IoT

The applications of social Internet of Things (SIoT) with large numbers of intelligent devices provide a novel way for social behaviors. Intelligent devices share images according to the groups of their specified owners. However, sharing images may cause privacy disclosure when the images are illegally distributed without owners’ permission. To tackle this issue, combining blind watermark with additive secret sharing technique, we propose a lightweight and privacy-preserving image sharing (LPIS) scheme with illegal distributor detection in SIoT. Specifically, the query user’s authentication information is embedded in two shares of the transformed encrypted image by using discrete cosine transform (DCT) and additive secret sharing technique. The robustness against attacks, such as JPEG attack and the least significant bit planes (LSBs) replacement attacks, are improved by modifying 1/8 of coefficients of the transformed image. Moreover, we adopt two edge servers to provide image storage and authentication information embedding services for reducing the operational burden of clients. As a result, the identity of the illegal distributor can be confirmed by the watermark extraction of the suspicious image. Finally, we conduct security analysis and ample experiments. The results show that LPIS is secure and robust to prevent illegal distributors from modifying images and manipulating the embedded information before unlawful sharing.
Web citation analysis of Library and Information Science and Communication and Media Studies journals : A comparative study

The present study examines the availability of web citations in scholarly journals of Library and Information Science and Communication and Media Studies. The journals were selected based on their high impact factor published between 2008 and 2017. A PHP script was used to crawl the Uniform Resource Locators collected from the references. A total of 12,251 articles were downloaded and 5,55,428 references were extracted. A total of 1,02,718 URLs were checked for their availability. Further, the lexical features of URL like file extension, path depth, character length and the top-level domain were determined. The research findings indicated that the percentage of web citations in articles has been continuously increased from 2008 to 2017 in both disciplines. The result of the accessibility check indicated that more number of URLs in CMS journal articles were accessible than in LIS journal articles. The majority of errors in both the disciplines were due to HTTP 404 error code (Not found error). The findings of the study will be helpful to authors, publishers and editorial staff to ensure that web citations will be accessible in future.
Plithogenic Cognitive Maps in Decision Making

Plithogenic sets introduced by Smarandache (2018) have disclosed new research vistas and this paper introduces the novel concept of plithogenic cognitive maps (PCM) and its applications in decision making. The new approach of defining instantaneous state neutrosophic vector with the confinement of indeterminacy to (0,1] is proposed to quantify the degree of indeterminacy. The resultant vector is obtained by applying instantaneous state vector through the connection matrix together with plithogenic operators comprising the contradiction degrees. The connection matrix is represented as fuzzy matrix and neutrosophic matrix and the resultant vector is determined by applying plithogenic fuzzy operators and plithogenic neutrosophic operators respectively. The obtained results are highly feasible in making the decision as it incorporates the contradiction degree of the conceptual nodes with respect to the dominant node. This research work will certainly pave the way for developing new approaches in decision making using PCM.
FastCFI: Real-time Control-Flow Integrity Using FPGA without Code Instrumentation

Control-Flow Integrity (CFI) is an effective defense technique against a variety of memory-based cyber attacks. CFI is usually enforced through software methods, which entail considerable performance overhead. Hardware-based CFI techniques can largely avoid performance overhead, but typically rely on code instrumentation, forming a non-trivial hurdle to the application of CFI. Taking advantage of the tradeoff between computing efficiency and flexibility of FPGA, we develop FastCFI, an FPGA-based CFI system that can perform fine-grained and stateful checking without code instrumentation. We also propose an automated Verilog generation technique that facilitates fast deployment of FastCFI, and a compression algorithm for reducing the hardware expense. Experiments on popular benchmarks confirm that FastCFI can detect fine-grained CFI violations over unmodified binaries. When using FastCFI on prevalent benchmarks, we demonstrate its capability to detect fine-grained CFI violations in unmodified binaries, while incurring an average of 0.36% overhead and a maximum of 2.93% overhead.
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework

Lossy compression algorithms are typically designed to achieve the lowest possible distortion at a given bit rate. However, recent studies show that pursuing high perceptual quality would lead to increase of the lowest achievable distortion (e.g., MSE). This paper provides nontrivial results theoretically revealing that, \textit{1}) the cost of achieving perfect perception quality is exactly a doubling of the lowest achievable MSE distortion, \textit{2}) an optimal encoder for the classic rate-distortion problem is also optimal for the perceptual compression problem, \textit{3}) distortion loss is unnecessary for training a perceptual decoder. Further, we propose a novel training framework to achieve the lowest MSE distortion under perfect perception constraint at a given bit rate. This framework uses a GAN with discriminator conditioned on an MSE-optimized encoder, which is superior over the traditional framework using distortion plus adversarial loss. Experiments are provided to verify the theoretical finding and demonstrate the superiority of the proposed training framework.
Keras Model for Text Classification in Amazon Review Dataset using LSTM

With the use of Ecommerce, Industry 4.0 is being effectively used in online product-based commercial transactions. An effort has been made in this article to extract positive and negative sentiments from Amazon review datasets. This will give an upper hold to the purchaser to decide upon a particular product, without considering the manual rating given in the reviews. Even the number words in an inherent positive review exceeds by one, where the present classifiers misclassify them under negative category. This article addresses the aforementioned issue by using LSTM (Long-Short-Term-Memory) model, as LSTM model has a feedback mechanism based progression unlike the other classifiers, which are dependent on feed-forward mechanism. For achieving better classification accuracy, the dataset is initially processed and a total of 100239 short and 411313 long reviews have been obtained. With the appropriate Epoch iterations, it is observed that, this proposed model has gain the ability to classify with 89% accuracy, while maintaining a non-bias between the train and test datasets. The entire model is deployed in TensorFlow2.1.0 platform by using the Keras framework and python 3.6.0.
Adaptive Interval Type-2 Fuzzy Controller Based Direct Torque Control of Permanent Magnet Synchronous Motor

This paper develops an adaptive type-2 fuzzy logic controller for direct torque control of a permanent magnet synchronous motor. The type-2 fuzzy logic systems are used for modeling th ...
Hierarchical VAEs Know What They Don’t Know

Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the training distribution. This seemingly paradoxical behavior has caused concerns over the quality of the attained density estimates. In the context of hierarchical variational autoencoders, we provide evidence to explain this behavior by out-of-distribution data having in-distribution low-level features. We argue that this is both expected and desirable behavior. With this insight in hand, we develop a fast, scalable and fully unsupervised likelihood-ratio score for OOD detection that requires data to be in-distribution across all feature-levels. We benchmark the method on a vast set of data and model combinations and achieve state-of-the-art results on out-of-distribution detection.
Analysis of the Current Situation and Characteristics of College Student “Online Fraud Cases”

In the internet era, the college student groups are internet users who use the internet more widely and deeply than the ordinary internet users. However, college students have insufficient social experience and live in a relatively simple living environment for a long time, which makes them have low ability to distinguish the true from the false, so they are easily targeted by cyber scammers. This paper analyzes the causes and the basic types of online fraud cases that happened to college students; the fraud case data are classified and sorted to extract the salient features of the telecom network fraud cases of college students and the common features of the victims. The paper provides suggestions for guiding college students to learn to screen online fraud and self-protect and to cultivate more comprehensive high-quality talents.
Agile Systems Engineering

There is no software without systems engineering. Software is always part of a bigger system. Even a simple application interacting with a human user consists of software building blocks, a service delivery, various hardware systems, user experience, energy management, connectivity and many more. Systems engineering or "systems thinking," as it is often called, ensures that products deliver more value than the capabilities of their individual parts. Such value beyond the individual components is what we call a system, or even a system of systems. A system is a set of interacting elements that together achieve a purpose.
Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks.

It is now established that modern neural language models can be successfully trained on multiple languages simultaneously without changes to the underlying architecture. But what kind of knowledge is really shared among languages within these models? Does multilingual training mostly lead to an alignment of the lexical representation spaces or does it also enable the sharing of purely grammatical knowledge? In this paper we dissect different forms of cross-lingual transfer and look for its most determining factors, using a variety of models and probing tasks. We find that exposing our LMs to a related language does not always increase grammatical knowledge in the target language, and that optimal conditions for lexical-semantic transfer may not be optimal for syntactic transfer.
Index-based Solutions for Efficient Density Peak Clustering (Extended Abstract)

Clusters reflect a potential relationship among different entities of data. This data can be sourced from a wide range of domains like market research, spatial data analysis, etc. Many clustering algorithms have been developed in the last few decades in response to the proliferating demands across industries and organizations, which help them make operational and strategic decisions. Among them, density-based clustering algorithms are popular, which find subsets of objects in "dense regions" separated by not-so-dense regions, where each subset represents a cluster. In this paper, our focal point will be Density Peak Clustering (DPC) [1] , a popular approach towards obtaining density-based clusters.
Transfer Learning for Sequence Generation: from Single-source to Multi-source

Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc.As MSG tasks suffer from the data scarcity problem and recent pretrained models have been proven to be effective for low-resource downstream tasks, transferring pretrained sequenceto-sequence models to MSG tasks is essential.Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained selfattention layers to capture cross-source information is not sufficient.Therefore, we propose a two-stage finetuning method to alleviate the pretrain-finetune discrepancy and introduce a novel MSG model with a fine encoder to learn better representations in MSG tasks.Experiments show that our approach achieves new state-of-the-art results on the WMT17 APE task and multi-source translation task using the WMT14 test set.When adapted to documentlevel translation, our framework outperforms strong baselines significantly.
A Performance Assessment of Free-to-Use Vulnerability Scanners - Revisited

Vulnerability scanning tools can help secure the computer networks of organisations. Triggered by the release of the Tsunami vulnerability scanner by Google, the authors analysed and compared the commonly used, free-to-use vulnerability scanners. The performance, accuracy and precision of these scanners are quite disparate and vary accordingly to the target systems. The computational, memory and network resources required be these scanners also differ. We present a recent and detailed comparison of such tools that are available for use by organisations with lower resources such as small and medium-sized enterprises.
Generalized One-Class Learning Using Pairs of Complementary Classifiers

One-class learning is the classic problem of fitting a model to the data for which annotations are available only for a single class. In this paper, we explore novel objectives for one-class learning, which we collectively refer to as Generalized One-class Discriminative Subspaces (GODS). Our key idea is to learn a pair of complementary classifiers to flexibly bound the one-class data distribution, where the data belongs to the positive half-space of one of the classifiers in the complementary pair and to the negative half-space of the other. To avoid redundancy while allowing non-linearity in the classifier decision surfaces, we propose to design each classifier as an orthonormal frame and seek to learn these frames via jointly optimizing for two conflicting objectives, namely: i) to minimize the distance between the two frames, and ii) to maximize the margin between the frames and the data. The learned orthonormal frames will thus characterize a piecewise linear decision surface that allows for efficient inference, while our objectives seek to bound the data within a minimal volume that maximizes the decision margin, thereby robustly capturing the data distribution. We explore several variants of our formulation under different constraints on the constituent classifiers, including kernelized feature maps. We demonstrate the empirical benefits of our approach via experiments on data from several applications in computer vision, such as anomaly detection in video sequences, human poses, and human activities. We also explore the generality and effectiveness of GODS for non-vision tasks via experiments on several UCI datasets, demonstrating state-of-the-art results.
We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at Scale

We propose to study Automating Science Journalism (ASJ), the process of producing a layman's terms summary of a research article, as a new benchmark for long neural abstractive summarization and story generation. Automating science journalism is a challenging task as it requires paraphrasing complex scientific concepts to be grasped by the general public. Thus, we create a specialized dataset that contains scientific papers and their Science Daily press releases. We demonstrate numerous sequence to sequence (seq2seq) applications using Science Daily with the aim of facilitating further research on language generation, which requires extreme paraphrasing and coping with long research articles. We further improve the quality of the press releases using co-training with scientific abstracts of sources or partitioned press releases. Finally, we apply evaluation measures beyond ROUGE and we demonstrate improved performance for our method over strong baselines, which we further confirm by quantitative and qualitative evaluation.
Digital Technologies in Chemistry Teaching: A Brief Review of the Available Categories and Tools

Advancements in digital information and communication technology has promoted transformations in several sectors of our society, including the educational area.For this reason, teaching methodologies need to be increasingly related to technology.In addition, emergency crisis situations (such as the current COVID-19 pandemic) have brought about drastic changes in the way we act, live, and teach-learn.In this sense, new educational approaches are being researched, developed and implemented, in an attempt to adapt education to incessant advances and global changes.Thus, in order to make a brief review, and at the same time to indicate paths for research or use of technological resources in teaching, this article reports, even in a modest way, some of the tools that can be used for teaching chemistry -at the different levels and educational modalities of this science.
The Application of Artificial Intelligence in Modern Foreign Language Learning

With the deepening of the globalization process, the study of various languages becomes necessary, and the application of artificial intelligence technology in foreign language learning has a huge space. This paper analyzes the application of artificial intelligence based on foreign language learning technology. We can design a comprehensive multi-angle artificial intelligence in multilingual language learning application of feasible technology to improve the efficiency and interest of foreign language learning for the future development Multi - angle foreign language learning system and its application to do bedding.
Modeling 3D Convolution Architecture for Actions Recognition

Abstract Action recognition infrastructure can be applied anywhere behavior analysis is required and represents presently a domain of maximum actuality in security and surveillance. The model based on 3D Convolutions is a middle ground between simple key-frame approaches based on 2D convolutions, and other more complex approaches based on Recurrent Neural Networks. Behavior analysis represents a domain greatly improved by action recognition. By placing human actions in different categories it is possible to extract statistics regarding a person’s behavior, characteristics, abilities and preferences which can be processed later by specialized personnel, depending on the selected domain. The proposed model follows simple 3D convolution architecture. Hidden layers are composed of a convolution operation, an activation function and, sometimes, a pooling layer. Leaky ReLU was used as activation function to alleviate the problem of vanishing gradients. Batch Normalization is a technique used for scaling and adjusting the output of an activation layer, and it has been used to reduce over-fitting and decrease the training time. The 3D Convolution structure has the advantage of learning spatio-temporal features, because the convolution is applied over a sequence of frames. In the present paper is presented a proposed 3D convolution model that has average results, with an accuracy of approximately 55% on the NTU RGB+D dataset.
Comparing Word Diversity Versus Amount of Speech in Parents’ Responses to Infants’ Prelinguistic Vocalizations

Our prior research posits that the prelinguistic vocalizations of infants may elicit caregiver speech which is simplified in its linguistic structure. Caregivers’ speech clearly contributes to infants’ development; infants’ communicative and cognitive development are predicted by their ambient language environment. There are at least two sources of variation in infants’ language environment: 1) the number and 2) the diversity of words infants hear. We compare the change in the total number of words (tokens) to the diversity of words against one another. Distributions of words of differing sizes are difficult to compare to one another because the size of the distribution largely determines the word diversity of the distribution. A novel approach to minimizing the challenges of comparing distributions of words is applied to data which were previously reported. We also conducted a new simulation study to estimate the probability that these results are expected by chance. We found that the linguistic structure of caregivers’ responses to infants’ prelinguistic vocalizations has fewer word types as compared to infant-directed but noncontingent speech. Our new method shows that contingent word distributions remain simplified as the number of total words sampled increases. By vocalizing, infants elicit caregiver speech, which is simpler in structure and may be easier to learn.