Reviewerly/exHarmony huggingface dataset:
Attention Aware Wavelet-based Detection of Morphed Face Images

Morphed images have exploited loopholes in the face recognition checkpoints, e.g., Credential Authentication Technology (CAT), used by Transportation Security Administration (TSA), which is a non-trivial security concern. To overcome the risks incurred due to morphed presentations, we propose a wavelet-based morph detection methodology which adopts an end-to-end trainable soft attention mechanism. Our attention-based deep neural network (DNN) focuses on the salient Regions of Interest (ROI) which have the most spatial support for morph detector decision function, i.e, morph class binary softmax output. A retrospective of morph synthesizing procedure aids us to speculate the ROI as regions around facial landmarks, particularly for the case of landmark-based morphing techniques. Moreover, our attention-based DNN is adapted to the wavelet space, where inputs of the network are coarse-to-fine spectral representations, 48 stacked wavelet sub-bands to be exact. We evaluate performance of the proposed framework using three datasets, VISAPP17, LMA, and MorGAN. In addition, as attention maps can be a robust indicator whether a probe image under investigation is genuine or counterfeit, we analyze the estimated attention maps for both a bona fide image and its corresponding morphed image. Finally, we present an ablation study on the efficacy of utilizing attention mechanism for the sake of morph detection.
Information Security Risk Assessment

Information security risk assessment is an important part of enterprises’ management practices that helps to identify, quantify, and prioritize risks against criteria for risk acceptance and objectives relevant to the organization. Risk management refers to a process that consists of identification, management, and elimination or reduction of the likelihood of events that can negatively affect the resources of the information system to reduce security risks that potentially have the ability to affect the information system, subject to an acceptable cost of protection means that contain a risk analysis, analysis of the “cost-effectiveness” parameter, and selection, construction, and testing of the security subsystem, as well as the study of all aspects of security.
Civic Community Archiving with the Platform for Experimental Collaborative Ethnography: Double Binds and Design Challenges

Community archives serve an array of purposes and types of communities (fan clubs, scientists in particular disciplines, ethnic neighborhoods). We discuss here civic community archives; civic archives, like "civic science," have expressly progressive political aims, question established order, and contribute to inclusive knowledge production and prosperity. Designing civic archives involves many types of analysis and poses many design challenges. In this paper, we share an analytic framework developed to guide the design of civic community archives, drawing on both cultural theory and our experience designing archives for different kinds of communities, with different purposes, within larger ethnographic projects. We question how to characterize "the community" in community archive projects, and the stakeholders in such projects. We ask what should be recollected in community archives and for what purposes. We also ask how, by design, community archives can connect diverse users, analog and digital components (including human and technological), and complicated pasts to creative futures. Throughout, we call out the double-binds of civic community archiving, delineating risks and possible pathologies as well as generative potential. We approach the work as cultural anthropologists and ethnographers involved in building the Platform for Experimental Collaborative Ethnography ( https://github.com/PECE-project/pece-distro ), open source digital infrastructure for sharing and collaborative analysis of ethnographic data.
A novel integrated AHP and MULTIMOORA method with interval-valued spherical fuzzy sets and single-valued spherical fuzzy sets to prioritize financial ratios for financial accounting fraud detection

Many studies have used different financial ratios for financial accounting fraud detection. This study focuses on multicriteria decision-making (MCDM) for ranking 25 financial ratios with respect to six criteria in detecting financial accounting fraud using interval-valued spherical fuzzy sets (IVSFS) and single-valued spherical fuzzy sets (SVSFS) to overcome uncertainties in decision-making process of financial analysts. This study proposes an integrated Analytic Hierarchy Process (AHP) and Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form (MULTIMOORA) approach using IVSFS and SVSFS. Comparative results are obtained and discussed in prioritization of financial ratios for both IVSFS and SVSFS.
Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset

Abstract As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (IBM Explorys), including demographics, comorbidities, symptoms, and hospitalization. Out of 15753 COVID-19 patients, 2050 went into critical state or deceased. Non-random train-test splits by time were repeated 100 times and led to a ROC AUC of 0.861 [0.838, 0.883] and a precision-recall AUC of 0.434 [0.414, 0.485] (median and interquartile range). The interpretability analysis confirmed evidence on major risk factors (e.g., older age, higher BMI, male gender, diabetes, and cardiovascular disease) in an efficient way compared to clinical studies, demonstrating the model validity. Such personalized predictions could enable fine-graded risk stratification for optimized care management.
Text Guide: Improving the Quality of Long Text Classification by a Text Selection Method Based on Feature Importance

The performance of text classification methods has improved greatly over the last decade for text instances of less than 512 tokens. This limit has been adopted by most state-of-the-research transformer models due to the high computational cost of analyzing longer text instances. To mitigate this problem and to improve classification for longer texts, researchers have sought to resolve the underlying causes of the computational cost and have proposed optimizations for the attention mechanism, which is the key element of every transformer model. In our study, we are not pursuing the ultimate goal of long text classification, i.e., the ability to analyze entire text instances at one time while preserving high performance at a reasonable computational cost. Instead, we propose a text truncation method called Text Guide, in which the original text length is reduced to a predefined limit in a manner that improves performance over naive and semi-naive approaches while preserving low computational costs. Text Guide benefits from the concept of feature importance, a notion from the explainable artificial intelligence domain. We demonstrate that Text Guide can be used to improve the performance of recent language models specifically designed for long text classification, such as Longformer. Moreover, we discovered that parameter optimization is the key to Text Guide performance and must be conducted before the method is deployed. Future experiments may reveal additional benefits provided by this new method.
Learning a family of motor skills from a single motion clip

We present a new algorithm that learns a parameterized family of motor skills from a single motion clip. The motor skills are represented by a deep policy network, which produces a stream of motions in physics simulation in response to user input and environment interaction by navigating continuous action space. Three novel technical components play an important role in the success of our algorithm. First, it explicitly constructs motion parameterization that maps action parameters to their corresponding motions. Simultaneous learning of motion parameterization and motor skills significantly improves the performance and visual quality of learned motor skills. Second, continuous-time reinforcement learning is adopted to explore temporal variations as well as spatial variations in motion parameterization. Lastly, we present a new automatic curriculum generation method that explores continuous action space more efficiently. We demonstrate the flexibility and versatility of our algorithm with highly dynamic motor skills that can be parameterized by task goals, body proportions, physical measurements, and environmental conditions.
Learnings from the design and acceptance of the German COVID-19 tracing app for IS-driven crisis management: a design science research

This article investigates the research problem of digital solutions to overcome the pandemic, more closely examining the limited effectiveness and scope of the governmental COVID-19 tracing apps, using the German COVID-19 tracing app (Corona-Warn-App) as an example. A well-designed and effective instrument in the technological toolbox is of utmost importance to overcome the pandemic.A multi-methodological design science research approach was applied. In three development and evaluation cycles, we presented, prototyped, and tested user-centered ideas of functional and design improvement. The applied procedure contains (1) a survey featuring 1993 participants from Germany for evaluating the current app, (2) a gathering of recommendations from epidemiologists and from a focus group discussion with IT and health experts identifying relevant functional requirements, and (3) an online survey combined with testing our prototype with 53 participants to evaluate the enhanced tracing app.This contribution presents 14 identified issues of the German COVID-19 tracing app, six meta-requirements, and three design principles for COVID-19 tracing apps and future pandemic apps (e.g., more user involvement and transparency). Using an interactive prototype, this study presents an extended pandemic app, containing 13 potential front-end (i.e., information on the regional infection situation, education and health literacy, crowd and event notification) and six potential back-end functional requirements (i.e., ongoing modification of risk score calculation, indoor versus outdoor). In addition, a user story approach for the COVID-19 tracing app was derived from the findings, supporting a holistic development approach.Throughout this study, practical relevant findings can be directly transferred to the German and other international COVID-19 tracing applications. Moreover, we apply our findings to crisis management theory-particularly pandemic-related apps-and derive interdisciplinary learnings. It might be recommendable for the involved decision-makers and stakeholders to forego classic application management and switch to using an agile setup, which allows for a more flexible reaction to upcoming changes. It is even more important for governments to have a well-established, flexible, design-oriented process for creating and adapting technology to handle a crisis, as this pandemic will not be the last one.
SPACE: Locality-Aware Processing in Heterogeneous Memory for Personalized Recommendations

Personalized recommendation systems have become a major AI application in modern data centers. The main challenges in processing personalized recommendation inferences are the large memory footprint and high bandwidth requirement of embedding layers. To overcome the capacity limit and bandwidth congestion of on-chip memory, near memory processing (NMP) can be a promising solution. Recent work on accelerating personalized recommendations proposes a DIMMbased NMP design to solve the bandwidth problem and increases memory capacity. The performance of NMP is determined by the internal bandwidth and the prior DIMM-based approach utilizes more DIMMs to achieve higher operation throughput. However, extending the number of DIMMs could eventually lead to significant power consumption due to inefficient scaling. We propose SPACE, a novel heterogeneous memory architecture, which is efficient in terms of performance and energy. SPACE exploits a compute-capable 3D-stacked DRAM with DIMMs for personalized recommendations. Prior to designing the proposed system, we give a quantitative analysis of the user/item interactions and define the two localities: gather locality and reduction locality. In gather operations, we find only a small proportion of items are highly-accessed by users, and we call this gather locality. Also, we define reduction locality as the reusability of the gathered items in reduction operations. Based on the gather locality, SPACE allocates highly-accessed embedding items to the 3D-stacked DRAM to achieve the maximum bandwidth. Subsequently, by exploiting reduction locality, we utilize the remaining space of the 3D-stacked DRAM to store and reuse repeated partial sums, thereby minimizing the required number of element-wise reduction operations. As a result, the evaluation shows that SPACE achieves 3.2× performance improvement and 56% energy saving over the previous DIMM-based NMPs leveraging 3D-stacked DRAM with a 1/8 size of DIMMs. Also, compared to the state-of-the-art DRAM cache designs with the same NMP configuration, SPACE achieves an average 32.7% of performance improvement.
Meta Balanced Network for Fair Face Recognition

Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this paper, we aim to systematically and scientifically study this bias from both data and algorithm aspects. First, using the dermatologist approved Fitzpatrick Skin Type classification system and Individual Typology Angle, we contribute a benchmark called Identity Shades (IDS) database, which effectively quantifies the degree of the bias with respect to skin tone in existing face recognition algorithms and commercial APIs. Further, we provide two skin-tone aware training datasets, called BUPT-Globalface dataset and BUPT-Balancedface dataset, to remove bias in training data. Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones. To determine the margins, our method optimizes a meta skewness loss on a clean and unbiased meta set and utilizes backward-on-backward automatic differentiation to perform a second order gradient descent step on the current margins. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition. The proposed datasets are available at http://www.whdeng.cn/RFW/index.html.
Multi-level and relevance-based parallel clustering of massive data streams in smart manufacturing

Parallel implementations of incremental clustering have been provided to increase performances of data stream processing in smart factories, to enable real-time anomaly detection, remote diagnosis, condition-based monitoring of Cyber-Physical Systems. Incremental clustering algorithms iteratively extract and update over time clusters of data points (often denoted as micro-clusters) whose maximum number is bounded. However, the capability of controlling costs derived from the exploitation of computational resources on the distributed architecture is challenging to enable a sustainable processing of massive data streams. In this paper, we present a multi-level parallelization approach for clustering massive data streams based on an horizontal scaling platform for Big Data processing. In particular, the following levels are considered: (i) a first parallelization level is based on a multi-dimensional model with exploration facets used to perform a first, coarse-grained partition of data streams, according to a divide-and-conquer strategy; (ii) a second parallelization level is based on a buffering mechanism, that splits the data stream into portions of data points on which processing is performed in parallel; (iii) the third level of parallelization is defined over the set of micro-clusters that are generated and change over time. The approach is conceived for anomaly detection in smart manufacturing, where the concept of data relevance, defined in terms of distance from critical conditions of monitored systems, is used in order to force a stronger parallelization (and therefore higher resource usage) only when necessary, that is, when approaching to critical conditions. The scalability and efficiency of the approach are evaluated using a real dataset in a smart factory scenario. In particular, experiments demonstrated that when the maximum number of allowed micro-clusters decreases and the buffer size increases, parallelization based on buffering does not ensure good scalability. Additionally, as the number of features (that is, the complexity of data stream) increases, the parallelization based on buffering may present scalability issues. This paves the way to the advantages of tuning different parallelization levels according to the approach proposed in this paper.
AnoML-IoT: An end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things

The rapid development in ubiquitous computing has enabled the use of microcontrollers as edge devices. These devices are used to develop truly distributed IoT-based mechanisms where machine learning (ML) models are utilized. However, integrating ML models to edge devices requires an understanding of various software tools such as programming languages and domain-specific knowledge. Anomaly detection is one of the domains where a high level of expertise is required to achieve promising results. In this work, we present AnoML which is an end-to-end data science pipeline that allows the integration of multiple wireless communication protocols, anomaly detection algorithms, deployment to the edge, fog, and cloud platforms with minimal user interaction. We facilitate the development of IoT anomaly detection mechanisms by reducing the barriers that are formed due to the heterogeneity of an IoT environment. The proposed pipeline supports four main phases: (i) data ingestion, (ii) model training, (iii) model deployment, (iv) inference and maintaining. We evaluate the pipeline with two anomaly detection datasets while comparing the efficiency of several machine learning algorithms within different nodes. We also provide the source code of the developed tools which are the main components of the pipeline.
Models on Teaching Effectiveness in Current Scientific Literature

Knowing what defines ‘effective teaching’ contributes significantly to the appropriateness and validity of the instruction provided to pre-service teachers during their initial training. This may help them to perform as expert teachers before finishing their university training. This study aims to reflect upon the most significant models and theoretical contributions of each of the five most common methods for investigating teaching effectiveness in the current scientific literature. To that end, the main scientific sources within this field of study have been obtained on the Internet through the Web of Science (WoS), Scopus and ERIC. The sources were selected for their relevance to the topic, which was determined by assessing the citations generated by the academic works and the impact of the journals in which they were published. The results show that the key and most central factor salient in teaching quality is related to the teachers themselves and their training. Thus, any educational model that seeks educational excellence must focus foremost on ensuring care and respect for teaching professionals, beyond economic investment, resource availability, or any other factors.
MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation

We contribute a deep-learning-based method that assists in designing analytical dashboards for analyzing a data table. Given a data table, data workers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts. This process is further complicated by the needs of creating dashboards composed of multiple views that unveil different perspectives of data. Existing automated approaches for recommending multiple-view visualizations mainly build on manually crafted design rules, producing sub-optimal or irrelevant suggestions. To address this gap, we present a deep learning approach for selecting data columns and recommending multiple charts. More importantly, we integrate the deep learning models into a mixed-initiative system. Our model could make recommendations given optional user-input selections of data columns. The model, in turn, learns from provenance data of authoring logs in an offline manner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the usefulness of the system.
Review on Quantum Communication and Quantum Computation

Abstract Quantum communication has made great breakthroughs in recent years. Because of its characteristics for strict information security transmission and high speed, it has received attention from related research fields around the world. This paper briefly reviews the progress in the field of quantum computing and communication. The basic functions of quantum mechanics related to the quantum communication are first introduced, including qubit, logic gates, postulates, polarization and quantum entanglement. Then the applications of quantum communication are discussed including teleportation, cryptography and quantum networks. Finally, advantages and disadvantages for the current applications are analyzed and the challenges remained in the current research are demonstrated.
An Efficient Implementation of KYBER

Quantum algorithms pose a huge threat to the current cryptosystems. In this article, we present a hardware implementation of CRYSTALS-KYBER which is one of the post-quantum cryptosystems based on the Module-LWE problem. Using the proposed modular reduction algorithm, modified modular adder and the reconfigurable data path, the design shares the computing resource for different polynomial related operations, and achieves higher degree of parallelism. Our design is implemented on a Xilinx Artix-7 FPGA. Compared with the leading hardware implementations, our design is more compact, the execution time is shorter, and it significantly consumes fewer registers.