Reviewerly/exHarmony huggingface dataset:
Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions

Attention mechanisms form the basis of providing temporal explanations for a variety of state-of-the-art recurrent neural network (RNN) based architectures. However, evidence is lacking that attention mechanisms are capable of providing sufficiently valid medical explanations. In this study we focus on the quality of temporal explanations for the medical problem of adverse drug event (ADE) prediction by comparing explanations globally and locally provided by an attention-based RNN architecture against those provided by more a more basic RNN using the post-hoc SHAP framework, a popular alternative option which adheres to several desirable explainability properties. The validity of this comparison is supported by medical expert knowledge gathered for the purpose of this study. This investigation has uncovered that these explanation methods both possess appropriateness for ADE explanations and may be used complementarily, due to SHAP providing more clinically appropriate global explanations and attention mechanisms capturing more clinically appropriate local explanations. Additional feedback from medical experts reveal that SHAP may be more applicable to real-time clinical encounters, in which efficiency must be prioritised, over attention explanations which possess properties more appropriate for offline analyses.
Help the User Recognize a Phishing Scam: Design of Explanation Messages in Warning Interfaces for Phishing Attacks

Remote work due to the COVID-19 pandemic is expected to be the new normal, suggesting a situation where people use their personal computers at home for several activities like reading emails, surfing the web, chatting with friends. While doing this, users are not focused on securing their systems and they often do not have the skills and knowledge to defend against cybercrime. In this paper, we present the design and the evaluation of a novel interface that warns users against phishing attacks. This interface looks like the ones shown by browsers like Chrome and Firefox when opening a suspicious phishing website, but it includes information that explains the reasons why the website might be a scam. Such explanations are based on website features commonly used by AI-based systems to classify a website as phishing or not and aim to help users detecting phishing websites. To ensure a high understandability and effectiveness of the explanations, the C-HIP model was adopted to design such messages, which have been iteratively refined performing a static analysis of their comprehension, sentiment, and readability.
An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN

Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment. The reward function may be hard to model, and successful experience transitions are difficult to find in experience replay. In this context, this paper proposes an improved Double DQN (DDQN) to solve the problem by reference to A* and Rapidly-Exploring Random Tree (RRT). In order to achieve the rich experiments in experience replay, the initialization of robot in each training round is redefined based on RRT strategy. In addition, reward for the free positions is specially designed to accelerate the learning process according to the definition of position cost in A*. The simulation experimental results validate the efficiency of the improved DDQN, and robot could successfully learn the ability of obstacle avoidance and optimal path planning in which DQN or DDQN has no effect.
Reducing Face-Touches to Limit COVID-19 Outbreak: an Overview of Solutions

The lack of consolidated therapeutic protocols to counteract the COVID-19 has drawn attention to the social role of hygiene rules to prevent the escalation of infections. Particular focus has been put on the importance of limiting face-touch occurrences. Given that SARS-CoV-2 transmission can occur by self-inoculation, impeding the virus to reach eyes, nose or mouth is paramount to prevent infections. To deal with this aspect, several methods for estimating hand proximity to face and notifying the user about face-touch occurrences have been developed. In this overview, we present the most relevant, ready-to-use, and large-scale deployable solutions. Most of them consist in applications running on smartwatches/smartphones or relying on wearable accessories, e.g. smartbands. Although the use for the COVID-19 emergency nowadays is the most urgent application, these solutions can be employed in ordinary daily life as simple tools to discourage face-touching, decreasing the probability to get infections from the environment, or as habit reversal therapy.
Dataflow Restructuring for Active Memory Reduction in Deep Neural Networks

The volume reduction of the activation maps produced by the hidden layers of a Deep Neural Network (DNN) is a critical aspect in modern applications as it affects the on-chip memory utilization, the most limited and costly hardware resource. Despite the availability of many compression methods that leverage the statistical nature of deep learning to approximate and simplify the inference model, e.g., quantization and pruning, there is room for deterministic optimizations that instead tackle the problem from a computational view. This work belongs to this latter category as it introduces a novel method for minimizing the active memory footprint. The proposed technique, which is data-, model-, compiler-, and hardware-agnostic, does implement a functional-preserving, automated graph restructuring where the memory peaks are suppressed and distributed over time, leading to flatter profiles with less memory pressure. Results collected on a representative class of Convolutional DNNs with different topologies, from Vgg16 and SqueezeNetV1.1 to the recent MobileNetV2, ResNet18, and InceptionV3, provide clear evidence of applicability, showing remarkable memory savings (62.9% on average) with low computational overhead (8.6% on average).
Optimization and Simulation of Labor Resource Management Information Platform Based on Internet of Things

This paper conducts an in-depth analysis and research on the optimization of the labor resource management information platform through the Internet of Things (IoT) technology; through the collection, classification, and data search functions of this application system, it meets the supply and demand of professional talents within a certain enterprise. At the same time, it also realizes the curriculum training application on improving the skills and literacy of the employees of a certain enterprise, and it can learn the enterprise curriculum training from the comments of the employees on the enterprise curriculum. The effect of the enterprise course training can be learned from the comments of the employees on the enterprise course, providing an important reference basis for the future revision of the enterprise course training content. The performance of the participants in the training also has objective data for reference, so that the situation will not be disconnected from reality, and the interaction between enterprise management and employees can achieve a balanced effect. The goal of this workforce resource management system is to create a systematic workforce resource management platform for professional talents and help enterprises achieve the goal of speeding up and increasing efficiency. The system interface provided by the third party is used for horizontal data expansion to realize the sharing of basic information or video data as well as system expansion to realize real-time monitoring and management of project works. The cloud platform realizes efficient management and scientific application of construction site projects by construction management departments, which better solves the current problem of lack of supervision at construction sites.
Cybercrime in the Context of COVID-19

The ongoing COVID-19 pandemic acts as a major cause of the attention of the whole world. All the aspects of life in our current world have been impacted by the advancement of information and communication technologies. Like governments, businesses, and transactions, most of the sectors in the world are done through online communications, and networking. This increased reliance on online activities has triggered a surge in various kinds of illicit activities targeting online users. In regard to cyberspace, offenders use the Internet as a tool to reveal and access vulnerabilities in the security systems of Internet users, which enables them to inflict harm through means of theft and other illegal acts. Various new techniques and regulations have been implemented in order to counteract COVID-19 online scams, but the rise of cybercrime has given offenders room to perform their illegalities. The main objective of this article is to address the cause of the online COVID-19 scams and analyze them accordingly. Secondly, it seeks to provide the contemporary techniques used by the scammers in harvesting information from the victims. And finally, by taking the USA and European Union as a case study, the article will highlight the possible and efficient ways to tackle COVID-19 scams. The article concludes with a discussion on the plausible strategies and steps that are available to protect Internet users from such scams and mitigate the associated risks.
A novel binary operator for designing medical and natural image cryptosystems

Image cryptosystems aim to secure the transmission of images in the presence of adversaries in the network. For ensuring confidentiality, images are encrypted to form unintelligible cipher images, the algorithms of which are much different from those for text data. Most image cryptosystems follow confusion–diffusion or complex architectures, which involves permutation and substitution of pixel values. Often these are done using chaotic maps and involve binary operations such as bitwise XOR, addition–subtraction, DNA operations, etc., each of which has certain limitations. Most of the cryptosystems for medical images proposed in the literature encrypt 8-bit compressed versions of the images rather than the standard 16-bit DICOM version, which results in loss of information. This paper presents a novel binary operation that can be used for all types of image cryptosystems — from DICOM medical to natural images using both conventional and DNA approaches. The use of this operation has been tested on a proposed DNA-based cryptosystem, which has novel steps for encryption to overcome the drawbacks of some existing ones. The cryptosystem has been tested on several images of varying pixel depth and dimensions, and the results obtained meet the standards of a secured image encryption system.
Real-time Text Stream Processing: A Dynamic and Distributed NLP Pipeline

In recent years, the need for flexible and instant Natural Language Processing (NLP) pipelines becomes more crucial. The existence of real-time data sources, such as Twitter, necessitates using real-time text analysis platforms. In addition, due to the existence of a wide range of NLP toolkits and libraries in a variety of programming languages, a streaming platform is required to combine and integrate different modules of various NLP toolkits. This study proposes a real-time architecture that uses Apache Storm and Apache Kafka to apply different NLP tasks on streams of textual data. The architecture allows developers to inject NLP modules to it via different programming languages. To compare the performance of the architecture, a series of experiments are conducted to handle OpenNLP, Fasttext, and SpaCy modules for Bahasa Malaysia and English languages. The result shows that Apache Storm achieved the lowest latency, compared with Trident and baseline experiments.
A Constrained Feature Selection Approach Based on Feature Clustering and Hypothesis Margin Maximization

In this paper, we propose a semisupervised feature selection approach that is based on feature clustering and hypothesis margin maximization. The aim is to improve the classification accuracy by choosing the right feature subset and to allow building more interpretable models. Our approach handles the two core aspects of feature selection, i.e., relevance and redundancy, and is divided into three steps. First, the similarity weights between features are represented by a sparse graph where each feature can be reconstructed from the sparse linear combination of the others. Second, features are then hierarchically clustered identifying groups of the most similar ones. Finally, a semisupervised margin-based objective function is optimized to select the most data discriminative feature from within each cluster, hence maximizing relevance while minimizing redundancy among features. Eventually, we empirically validate our proposed approach on multiple well-known UCI benchmark datasets in terms of classification accuracy and representation entropy, where it proved to outperform four other semisupervised and unsupervised methods and competed with two widely used supervised ones.
Fine-Grained and Controllably Editable Data Sharing With Accountability in Cloud Storage

With the increasing cloud storage service, users can enjoy non-interactive data sharing. Nonetheless, the data owner cannot timely update the shared data all the while. To ensure the timeliness and the authoritative source of the data, some users should be allowed to update the data on behalf of an authoritative data owner without changing data source. However, this allows harmful information to be injected into the data unnoticeably. How to efficiently realize editable cloud-based data sharing supporting malicious user tracing has not been fully explored. To address the problem, we propose a fine-grained and controllably editable cloud-based data sharing scheme with malicious user accountability. The data owner only needs to sign the shared data before uploading it and can specify a fine-grained access control policy about who can update the data and which portions of the data can be updated. The authorized users non-interactively convert signatures of original data into new ones for the updated data, which are indistinguishable from the original signatures. The proposed scheme also supports malicious user accountability in the sense that malicious users who post harmful information can be traced. We demonstrate the security and practicality of our scheme via formal security analysis and extensive experiments.
A novel reversible fragile watermarking in DWT domain for tamper localization and digital image authentication

In recent years, altering and tampering with digital images have become easier with the swift development of internet and computer technologies. Therefore, the use of image authentication in legal cases, digital forensic, and medical imaging has become of paramount importance. In this paper, as a solution to this problem, we propose an MD5 Hash-based blind reversible fragile watermarking method. We divide the input image into non-overlapped 4×4 blocks. Then, the watermark information generated from blocks using the MD5 hash function is embedded in the one-level DWT high and middle frequency sub-bands. Our experimental results demonstrate that the proposed method can provide reversibility for applications such as medical, forensic medicine, and military fields where the original image is essential. It can also provide the ability to resist various malicious attacks such as exchange of content, crop, text addition, copy-paste, rotation, content removal, and noise addition.
An Effective Deep Learning Approach for Personalized Advertisement Service Recommend

Personalized advertisement service recommend represents one of the largest scales and most sophisticated industrial recommendation systems. The key challenge of advertisement recommend is to find relevant users for a specific advertisement. Traditional recommendation approaches suffered from selecting effective features on both advertisements and user profiles. In this paper, we studied a deep neural network to learning effective representation on advertisement recommend. Specifically, we connected one user profile features and one advertisement features together as one input vector, and then employed deep neural network to predict the whether the user is relevant to the advertisement. We conducted our experiments on Tencent advertising competition data set, and the experiment results show that (1) the DNN method obtained better predictive performance than traditional approaches; (2) the DNN method with 6 layers hidden nodes achieved best performance; (3) the single-perception method overcame the multi-perceptron method on Advertisement Recommend.
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems

Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of domain knowledge in the few-bit quantization process. While the first data detection network, B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the indeterminant gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into the weight matrices of its channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gains in estimation accuracy by the proposed deep learning framework.
Trends in the Development of Continuing Education: a Regional Aspect

The article discusses the trends in the development of continuing education. The study of the experience of residents of the Lipetsk region in the context of continuing education was conducted. Continuing education, in its various forms, is being updated as a mechanism for developing the region's human resources potential in the digital economy. The assessment of the degree of demand for various types of continuing education by specialists of the Lipetsk region was carried out.
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference

Gaussian processes (GPs) are a well-known nonparametric Bayesian inference technique, but they suffer from scalability problems for large sample sizes, and their performance can degrade for non-stationary or spatially heterogeneous data. In this work, we seek to overcome these issues through (i) employing variational free energy approximations of GPs operating in tandem with online expectation propagation steps; and (ii) introducing a local splitting step which instantiates a new GP whenever the posterior distribution changes significantly as quantified by the Wasserstein metric over posterior distributions. Over time, then, this yields an ensemble of sparse GPs which may be updated incrementally, and adapts to locality, heterogeneity, and non-stationarity in training data. We provide a 1-dimensional example to illustrate the motivation behind our approach, and compare the performance of our approach to other Gaussian process methods across various data sets, which often achieves competitive, if not superior predictive performance, relative to other locality-based GP regression methods in which hyperparameters are learned in an online manner.
Transcript to Video: Efficient Clip Sequencing from Texts

Among numerous videos shared on the web, well-edited ones always attract more attention. However, it is difficult for inexperienced users to make well-edited videos because it requires professional expertise and immense manual labor. To meet the demands for non-experts, we present Transcript-to-Video -- a weakly-supervised framework that uses texts as input to automatically create video sequences from an extensive collection of shots. Specifically, we propose a Content Retrieval Module and a Temporal Coherent Module to learn visual-language representations and model shot sequencing styles, respectively. For fast inference, we introduce an efficient search strategy for real-time video clip sequencing. Quantitative results and user studies demonstrate empirically that the proposed learning framework can retrieve content-relevant shots while creating plausible video sequences in terms of style. Besides, the run-time performance analysis shows that our framework can support real-world applications. Project page: http://www.xiongyu.me/projects/transcript2video/
Marginal Attacks of Generating Adversarial Examples for Spam Filtering

Digit information has been used in many areas and has been widely spread in the Internet era because of its convenience. However, many ill-disposed attackers, such as spammers take advantage of such convenience to send unsolicited information, such as advertisements, frauds, and pornographic messages to mislead users and this might cause severe consequences. Although many spam filters have been proposed in detecting spams, they are vulnerable and could be misled by some carefully crafted adversarial examples. In this paper, we propose the marginal attack methods of generating such adversarial examples to fool a naive Bayesian spam filter. Specifically, we propose three methods to select sensitive words from a sentence and add them at the end of the sentence. Through extensive experiments, we show that the generated adversarial examples could largely reduce the filter’s detecting accuracy, e.g. by adding only one word, the accuracy could be reduced from 93.6% to 55.8%. Furthermore, we evaluate the transferability of the generated adversarial examples against other traditional filters such as logic regression, decision tree and linear support vector machine based filters. The evaluation results show that these filters’ accuracy is also reduced dramatically; especially, the decision tree based filter’s accuracy drops from 100% to 1.51% by inserting only one word.
Robust and high-capacity audio watermarking basedon chirp z-transform

A novel non-blind audio watermarking algorithm, using chirp z-transform in combination with discrete wavelet transform and singular value decomposition, is presented in this paper. In the proposed method, a watermark audio signal (about 13 seconds in length) is hidden into an original audio signal (about 51 seconds in length) by applying discrete wavelet transform, chirp z-transform, and singular value decomposition. The effectiveness of the proposed algorithm has been tested using signal-to-noise ratio, objective difference grade (ODG), and normalised cross- correlation of watermarked audio signal under various common audio attacks. Experimental results demonstrate that the algorithm has a high capacity (352.8 kbps), without significant perceptual distortion (ODG > -0.29) and provides good robustness against common audio attacks, including: re-sampling, echo addition, MP3 compression, additive white Gaussian noise, re- quantisation, cutting, and low-pass filtering. This algorithm has a high capacity and excellent audio quality compared with other audio watermarking algorithms.