Reviewerly/exHarmony huggingface dataset:
Play Fair: Frame Attributions in Video Models

In this paper, we introduce an attribution method for explaining action recognition models. Such models fuse information from multiple frames within a video, through score aggregation or relational reasoning. We break down a model’s class score into the sum of contributions from each frame, fairly. Our method adapts an axiomatic solution to fair reward distribution in cooperative games, known as the Shapley value, for elements in a variable-length sequence, which we call the Element Shapley Value (ESV). Critically, we propose a tractable approximation of ESV that scales linearly with the number of frames in the sequence.We employ ESV to explain two action recognition models (TRN and TSN) on the fine-grained dataset Something-Something. We offer detailed analysis of supporting/distracting frames, and the relationships of ESVs to the frame’s position, class prediction, and sequence length. We compare ESV to naive baselines and two commonly used feature attribution methods: Grad-CAM and Integrated-Gradients.
Towards an Explainable Mortality Prediction Model

Influence functions are analytical tools from robust statistics that can help interpret the decisions of black-box machine learning models. Influence functions can be used to attribute changes in the loss function due to small perturbations in the input features. The current work on using influence functions is limited to the features available before the last layer of deep neural networks (DNNs). We extend the influence function approximation to DNNs by computing gradients in an end-to-end manner and relate changes in the loss function to individual input features using an efficient algorithm. We propose an accurate mortality prediction neural network and show the effectiveness of extended influence functions on the eICU dataset. The features chosen by proposed extended influence functions were more like those selected by human experts than those chosen by other traditional methods.
Geospatial Clustering and Network Design for Rural Electrification in Africa

This paper proposes automated geospatial clustering and network design methods for rural electrification in Africa. First, clustering techniques are proposed to geographically group homes into communities for grid design. These methods dynamically adjust to regional housing density, taking the legal definition of under-grid distance as the sole user input. Next, graph theory is applied to generate a preliminary distribution network design using a minimum spanning tree of home locations. Community distribution grids are interconnected with transmission lines using a secondary minimum spanning tree of community centroids. A best-fit electrification architecture (e.g. mini-grid, grid extension, etc.) can be proposed for each community based on its density, quantity of potential connections, anticipated infrastructure costs, and distance to grid. These methods demonstrate the detail achievable using home-level geospatial vector data in rural electrification planning instead of commonly-used gridded raster data. These techniques are illustrated through a case study near Koidu in Sierra Leone.
A Graph Data Privacy-Preserving Method Based on Generative Adversarial Networks

We proposed a graph anonymization method which is based on a feature learning model of Generative Adversarial Network (GAN). We used the differential privacy to ensure the privacy and take both anonymity and utility into consideration. The method consists of the following two parts: Firstly, we designed a graph feature learning method based on GAN. The method used the bias random walk strategy to sample the node sequence from graph data, and trained the GAN model. After training, the GAN generated a set of simulation sequences that are highly like the real sampled sequence. Secondly, we proposed an anonymous graph construction method based on the simulation node sequence. We calculated the number of edges in the node sequences and constructed a probability adjacency matrix. The differential privacy noise is added to get the anonymous probability adjacency matrix. Then we extract the edges from the anonymous matrix and then constructed the anonymous graph. We evaluate our methodology, showing that the model had good feature learning ability through embedding visualization and link prediction experiments, compared with other anonymous graphs. Through experiments such as metric evaluation, community detection, and de-anonymization attack, we proved that the anonymous method we proposed is better than the current mainstream anonymous method.
Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Prospective display advertising poses a particular challenge for large advertising platforms. The existing machine learning algorithms are easily biased towards the highly predictable retargeting events that are often non-eligible for the prospective campaigns, thus exhibiting a decline in advertising performance. To that end, efforts are made to design powerful models that can learn from signals of various strength and temporal impact collected about each user from different data sources and provide a good quality and early estimation of users' conversion rates. In this study, we propose a novel deep time-aware approach designed to model sequences of users' activities and capture implicit temporal signals of users' conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other, previously proposed approaches by a significant margin while providing interpretability of signal impact to conversion probability.
Board Game Design for Python Programming Education

In the traditional education system, most students sit and listen to a teacher's oral presentation. However, it is often difficult to increase student interest in learning using this method. Most students often become distracted during the sessions. A set of board games were designed and used in after-class learning to increase students' learning motivation. When playing the games, students had discussions with team members and learned from each other. Therefore, students learned and taught mutually. Studies have indicated that board games can be useful in learning Python programming. Thus, the proposed board games can promote programming concepts and solve problems in learning programming. According to the results, the proposed board games exhibited an excellent influence on student's learning goals.
Blind deconvolution via complementarily structure-aware image smoothing

Blind deconvolution is known as a challenging low-level vision problem due to the diverse blur scenarios in real-world imaging. Another attempt is made with critical thoughts on existing image priors for nonparametric blur kernel estimation, proposing an alternative approach to blind deconvolution via complementarily structure-aware image smoothing (CSIS). Similar to most state-of-the-art blind deblurring methods, the proposed approach partly builds on the naïve L0-based sparse model, but the core contribution here is to additionally advocate a type of redescending potential (RDP) functions as a more elegant way for boosting blind deblurring. With the RDP element, the new approach is capable of easily achieving the discrimination between clear and blurred images. Meanwhile, the performance of the proposed method can be better ensured due to the complementary smoothing behavior induced by the RDP functions. Specifically speaking, it is known that L0-based smoothing plays a critical role in pursuing salient step-edges from blurry images especially in the early period of blur kernel estimation, whereas the RDP-based smoothing is found particularly significant in the later stage when the fine pursuit of salient roof-edges or ramp-edges are dominantly critical for more precise and robust kernel estimation. In this sense, the proposed CSIS-based blind deblurring algorithm is more intuitive than previous L0-based methods. Not only that, numerous experimental results on benchmark blurred images, no matter synthetic or realistic, also demonstrate the comparable or even better performance of the proposed approach in terms of both effectiveness and robustness.
A Clustering-Based Collaborative Filtering Recommendation Algorithm via Deep Learning User Side Information

Collaborative filtering (CF) is a widely used recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item ratings can be problematic in many domains, limiting the ability to produce accurate and effective recommendations. Moreover, in some CF approaches only rating information is used to represent users and items, which can lead to a lack of recommendation explained. In this paper, we present a novel deep CF-based recommendation model, which co-learns users’ abundant attributes. To better understanding the user’s preference, we explore user deeper and unseen factors on the user-item ratings and user’s side information by adopting the AutoEncode network. After that, we conduct the k-mean algorithm with extracted deep user factors to classify users. Then the user-side CF algorithm is employed to produce the recommendation list based on the classification results, for alleviating recommendation speed. Finally, we conduct lots of experiments on real-world datasets. Compared with state-of-the-art methods, the results show that the proposed method has a significant improvement in recommendation performance, in terms of recommendation accuracy and diversity. Furthermore, it also enjoys high effectiveness, and the approach is useful when it comes to assigning intuitive meanings to improve the explainability of recommender systems.
My Boss is Really Cool: Malware-Induced Misperception in Workplace Communication Through Covert Linguistic Manipulation of Emails

This paper introduces a social engineering attack called Malware-Induced Misperception (MIM). Social engineering attacks are usually carried over emails and "phish" for the receiver's assets, e.g. passwords. The MIM also uses email as an attack vector but instead "phishes" for the receiver's perception. Since email is one of the main lines of communication in a workplace, individuals are trained how to spot traditional phishing but not emails aiming to induce misperception. A study was conducted to test the effect of the MIM attack in covertly manipulating the perception of the individual's working relationship in a realistic workplace email interaction (N = 173). The misperception-inducing malware was packaged as a browser extension that manipulated the "politeness" formatting of an email request through covert linguistic rearrangements. The results indicate that the attack is capable of creating misperceptions that: (1) an email request seems more demanding than it actually is; and (2) the sender seems more polite and "cool" than they actually are. The results also show that the attack nudges the receivers to match the level of politeness in their response to the one in the email request. The overall findings are consistent with the politeness theory applied in computer-mediated communication settings.
Compassionate Computing in the Time of COVID-19: Interview with Laurie N. Taylor

When COVID-19 was recognized as a global pandemic and offices, clinics, and academic centers closed in the spring of 2020, many librarians found themselves answering reference questions, teaching, meeting, facilitating access to information resources, and organizing resources remotely. In the transition from traditional offices and workspaces to work from home, librarians and their administrations faced many challenges and adapted to a variety of new technologies. Video-conferencing and phone-forwarding tools were being used in new ways or for the first time. Instead of addressing technology that librarians adopted or adapted in greater numbers during the global pandemic, this column will examine an attitude of care and approach to technology called "compassionate computing" that helped ease the transition to work from home during an unprecedented time.
Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-Based Recommendation in E-Commerce

Boosting the sales of e-commerce services is guaranteed once users find more items matching their interests in a short amount of time. Consequently, recommendation systems have become a crucial part of any successful e-commerce service. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-based recommendation systems in recent years. This growing interest is due to security concerns over collecting personalized user behavior data, especially due to recent general data protection regulations. In this work, we present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation. In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items that are more likely to correlate with their preferences. Our extensive experiments investigate baseline techniques (e.g., nearest neighbors and pattern mining algorithms) and deep learning approaches (e.g., recurrent neural networks, graph neural networks, and attention-based networks). Our evaluations show that advanced neural-based models and session-based nearest neighbor algorithms outperform the baseline techniques in most scenarios. However, we found that these models suffer more in the case of long sessions when there exists drift in user interests, and when there are not enough data to correctly model different items during training. Our study suggests that using the hybrid models of different approaches combined with baseline algorithms could lead to substantial results in session-based recommendations based on dataset characteristics. We also discuss the drawbacks of current session-based recommendation algorithms and further open research directions in this field.
Knowledge Management for Promoting Update of Systematic Literature Reviews: An Experience Report

Context: Systematic Literature Reviews (SLRs) are important instruments for both Software Engineering (SE) practitioners and scientific community. Their value directly depends on their quality and up-to-date results. However, most of the SLRs are outdated and the current scenario on how SLRs are documented does not favor their updating process. Goal: In this scenario, the main goal of this paper is to present an experience report on how to transfer the know-how of SLRs to facilitate their updates. Method: To address this issue, we used a Knowledge Management (KM) model, known as Nonaka-Takeuchi model, and described how we instantiated the Model for SLR update. We use two SLRs updates conducted by us to illustrate some of the knowledge sharing issues. Results: Our examples showed that the introduction of the concept of KM in the SLR update is in fact valuable, especially for sharing tacit knowledge (decisions) taken throughout the review process. Conclusions: We conclude that KM principles can be applied to manage the knowledge generated during the update of SLR.
Image Quality Assessment using Selective Contourlet Coefficients

The ubiquity of digital images getting captured and viewed everyday in today's world continuously increases the importance of image processing or image quality assessment to be precise. In this paper, we present an algorithm for Non-Referral Image Quality Assessment (NR-IQA). We make use of the information available at various levels of contourlet transform of a degraded image to predict the level of distortion. We use the simple statistical values of the contourlet coefficients of images to train Neural Network Model which gives us the level of degradation present in the corresponding image. This variable extraction and training algorithm has been found to show high correlation with standard datasets. Benchmarking and validation on the TID2013 dataset indicates that the proposed algorithm outperforms the state of the art algorithms.
Experimental Demonstration of Free Space Quantum Key Distribution System based on the BB84 Protocol

Quantum key distribution (QKD) has emerged as a revolutionary technique that can ensure unconditionally secure communication between two distant parties, referred to as Alice and Bob, by exploiting the fundamental principles of quantum physics. Here, we report experimental demonstration of a fully automated QKD system based on the BB84 protocol. A weak coherent pulse source is developed to generate stream of polarization encoded single photons. Our QKD system demonstrates a low QBER of ∼1.2% with sifted key generation rate of ∼70 Kbps at an average photon number per pulse of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu=0.11$</tex> . The final secure key rate of ∼33 Kbps is achieved after performing post processing procedures viz. error correction and privacy amplification. The quantum communication link is established continuously for several hours and the performance is evaluated iteratively. The developed QKD system is shown to have the capability of distributing cryptographic keys securely and seamlessly, under practical operating conditions. Furthermore, we have successfully demonstrated unconditionally secure transmission and reception of an image between Alice and Bob connected over an unsecured public channel by utilizing quantum cryptography.
Regularizing Neural Networks by Stochastically Training Layer Ensembles

Dropout and similar stochastic neural network regularization methods are often interpreted as implicitly averaging over a large ensemble of models. We propose STE (stochastically trained ensemble) layers, which enhance the averaging properties of such methods by training an ensemble of weight matrices with stochastic regularization while explicitly averaging outputs. This provides stronger regularization with no additional computational cost at test time. We show consistent improvement on various image classification tasks using standard network topologies.
Efficiently Training Intelligible Models for Global Explanations

Generalized additive models (GAMs) are one of the popular methods of building intelligible models on classification and regression problems. Fitting the most accurate GAMs is usually done via gradient boosting with bagged shallow trees. However, such method can be expensive for large industrial applications. In this work, we aim to improve the training efficiency of GAM. To this end, we propose to use subsample aggregating (subagging) in place of bootstrap aggregating (bagging). Our key observation is that subsamples of reasonable size (e.g., 60% of the training set) usually overlap. Such property allows us to explore the computation ordering inside a subagged ensemble and we present a novel algorithm to speed up the computation of subagged ensemble with no loss of accuracy. Our experimental results on public datasets demonstrate that our proposed method can achieve up to 3.7x speedup over bagged ensembles with comparable accuracy. Finally, we demonstrate our methodology of finding global explanations on a real application at Alipay. We have developed several strategies from the findings of those explanations and found those strategies achieved significant lift on key metrics through online experiments.
Remote User Authentication and Issues: A Survey

The most convenient and the simplest authentication method to deal with the important and secret data over an uncertain network is remote user authentication. For remote user authentication, a system call with a high-security network is required, which will help to identify whether the parties which are communicating are trustworthy and genuine. User and the server will be securely authenticated using remote user authentication, protecting and processing the communication in a user-friendly way, and provide security to the user by hiding their details. This paper discusses the different approaches in which a remote user authentication mechanism can be done and the issues in those methods, which will verify both the parties in a secure, simple, and convenient way.
Visualization of Emergency Needs Posted on Social Media by Metaphor Map

Thematic analysis based on a social network is one of the effective means in emergency management. To improve users' understanding, the results of the thematic analysis are often displayed through visualization. However, the previous researches on text theme visualization rarely considered the unified representation of theme structure and theme evolution. Cognitive load leads to difficulty, and it happens due to the separate representation of structure and evolution relationship and it still increases because of the characteristics of urgency, uncertainty, etc. Therefore, a metaphor map is introduced in this study to overcome the limits of previous visualization tools in characterizing the structure and evolutionary relationship of emergency. On the one hand, different elements in the metaphor map represent the information of popularity and structure of the demand, respectively. On the other hand, the visual design based on the metaphor map strengthens the representation of the content and evolution states of the demand themes. At the theoretical level, a visualization method for emergency needs based on the metaphor map is proposed in this study, which enriches the theory of emergency information visualization. At the practical level, this study explores the design of a visualization system based on the metaphor map under crisis scenarios, which enhances the interaction between users and crisis information, and provides references for decision-making such as emergency material scheduling and emergency resource coordination.
Recent Advancements in Text Detection Methods from Natural Scene Images

Effect of digitization and globalization has narrowed down the gap of geographical boundaries.Text/language plays an important role in getting connected with people utilizing oral or written communication.Nowadays text data is easily available in the form of multimedia e.g.audio, videos.A technique is needed to understand and interpret the text present in the videos/images which are rich in contents compared to audio data.Text detection and recognition are two main steps of such text-based applications.Text detection from natural scene images is tedious compared to text detection from document images.Various methods are available for text detection from natural scene images.Text detection methods are generally script specific.The main purpose of this paper is to highlight available text detection methods with pros and cons, challenges in the text detection process, evaluation parameters as well as recent achievements.The paper will act as a roadmap for upcoming researchers to select an appropriate text detection method.
Goal Modelling Meets Service Choreography: A Graph Transformation Approach

As microservices become one of the predominant architectural styles for distributed enterprise computing, there is a need to devise frameworks which allow for the goal driven composition and coordination of such highly granular service components. Even though a number of service composition and orchestration techniques have been proposed over the past decade, these do not take into account stakeholders’ intents as well as data, control, and temporal interdependencies between actions microservices can perform. In this paper, we present extensions to goal models with respect to data, logical and temporal dependencies exhibited between tasks and actions among microservices, and we propose a framework based on a graph transformation approach which, when applied to the extended goal models, can yield service invocation plans that achieve the desired requirements and constraints denoted by the specific goal models being considered.