Reviewerly/exHarmony huggingface dataset:
Forensic Analysis of the Cisco WebEx Application

The COVID-19 pandemic has triggered a surge in the usage of videoconferencing applications around the globe. While this trend provided a convenient alternative to face-to-face meetings, it has also opened the door for new scenarios of malicious attacks. The security and privacy of the (vidéoconférence) participants' data has therefore become a major concern. Despite its importance, the forensic analysis of videoconferencing applications remains a relatively under researched area. This paper presents a detailed analysis of the Cisco WebEx videoconferencing application on a Windows OS in the areas of memory forensics, disk-space forensics and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different sources. These include user emails, user IDs, profile photos, sent and deleted chat messages, shared media, meeting information including meeting passwords, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and log files. Although network communications are encrypted, some useful artifacts can be retrieved such as IPs of server domains and host devices along with message/event timestamps. Digital certificates of the videoconferencing communications are also retrieved.
Towards Understanding Developers’ Machine-Learning Challenges: A Multi-Language Study on Stack Overflow

Machine Learning (ML) is increasingly being used as an essential component of modern software systems. Also, the maturity of the adopted techniques and the availability of frameworks have changed the way developers approach ML-related development problems. This paper aims at investigating, by analyzing Stack Overflow (SO) posts related to ML, how the questions about ML have been changing over the years, and across six different programming languages. We analyzed 43,950 SO posts in the period 2008-2020, studying (i) how the number of ML-related posts changes over time for each programming language, (ii) how the posts are distributed across different phases of a ML pipeline, and (iii) whether posts belonging to different languages or phases are more or less challenging to address. We found that some programming languages are fading while others are becoming more popular in ML development. While model-building questions are the most discussed in general, the level of challenges posed by the other phases of the ML pipeline appears to be language-dependent. Results of this work could be used to better understand ML challenges in different programming languages, and, possibly, to improve ML tutorials related to different languages.
The Implicit Graph Conjecture is False

An efficient implicit representation of an n-vertex graph G in a family $\mathcal{F}$ of graphs assigns to each vertex of G a binary code of length O(log n) so that the adjacency between every pair of vertices can be determined only as a function of their codes. This function can depend on the family but not on the individual graph. Every family of graphs admitting such a representation contains at most $2^{O(n\log(n))}$ graphs on n vertices, and thus has at most factorial speed of growth. The Implicit Graph Conjecture states that, conversely, every hereditary graph family with at most factorial speed of growth admits an efficient implicit representation. We refute this conjecture by establishing the existence of hereditary graph families with factorial speed of growth that require codes of length $n^{\Omega(1)}$.
Configurable Butterfly Unit Architecture for NTT/INTT in Homomorphic Encryption

This paper proposes a configurable architecture of butterfly unit (BU) supporting number theoretic transform (NTT) and inverse NTT (INTT) accelerators in the ring learning with error based homomorphic encryption. The proposed architecture is fully pipelined and carefully optimized the critical path delay. To compare with related works, several BU designs of different bit-size specific primes are synthesized and successfully placed-and-routed on the Xilinx Zynq UltraScale+ ZCU102 FPGA platform. Implementation results show that the proposed BU designs achieve 3&#x00D7; acceleration with more efficient resource utilization compared with previous works. Thus, the proposed BU architecture is worthwhile to develop NTTINTT accelerators in advanced homomorphic encryption systems.
Pitch It Right: Using Prosodic Entrainment to Improve Robot-Assisted Foreign Language Learning in School-Aged Children

Robot-assisted language learning (RALL) is a promising application when employing social robots to help both children and adults acquire a language and is an increasingly widely studied area of child–robot interaction. By introducing prosodic entrainment, i.e., converging the robot’s pitch with that of the learner, the present study aimed to provide new insights into RALL as a facilitative method for interactive tutoring. It is hypothesized that pitch-level entrainment by a Nao robot during a word learning task in a foreign language will result in increased learning in school-aged children. The results indicate that entrainment has no significant effect on participants’ learning, contra the hypothesis. Research on the implementation of entrainment in the context of RALL is new. This study highlights constraints in currently available technologies for voice generation and methodological limitations that should be taken into account in future research.
Location-Based Games for Cultural Heritage: Applying the Design Thinking Process

Location-based games can engage visitors of a cultural site in learning and sensitization activities about local heritage. This paper reflects on the design thinking approach followed to develop a mobile learning game about the cultural heritage (CH) of industrial oil production in Lesvos Island, Greece. The game starts in the museum and continues in the settlement, where users (players) are presented with location-based learning missions and challenges about tangible and intangible CH. Empathy about this project was sparked with field visits in the museum and the settlement that increased team motivation and established several cooperation practices among members and local experts. Definition of the problem focused on system architecture. Ideation involved several iterations of the core team about focused design issues and a few larger in-scope iterations of the whole project team. The design of the mobile game emphasizes exploratory learning and storytelling including digital characters and narratives, as well as rewards in augmented reality (AR), where users view traditional tools in 3D. Prototyping involved technical experimentations, lab, and field prototypes. Testing was an empirical process that gradually developed from inspections to field playtesting involving both local experts and visitors. We reflect on lessons learned about the design thinking process, re-iterating on important principles and guidelines for cultural heritage projects.
Intelligent Arabic-Based Healthcare Assistant

Text classification has been one of the most common natural language processing (NLP) objectives in recent years. Compared to other languages, this mission with Arabic is relatively restricted and in its early stages, and this combination in the medical application area is rare. This paper builds an Arabic health care assistant, specifically a pediatrician that supports Arabic dialects, especially Egyptian accents. The proposed application is a chatbot based on Artificial Intelligence (AI) models after experimenting with Two Bidirectional Encoder Representations from Transformers (BERT) models, a pre-trained BERT and Logistic regression TF-IDF and Doc2vec. These models were applied to the Arabic dataset with different dialects from different couturiers such as Egypt, Saudi Arabia, and Iraq. The proposed system consists of 4 stages: scrapping and collecting data, then wrangling it, data preprocessing, data extraction, trained models with new data, and connect the model to the database that contains the answers. Experimental tests showed that the BERT model outperformed the others by getting a 95&#x0025; Accuracy. Logistic regression with Doc2vec was the second best with 71&#x0025; F-measure and 73&#x0025; Accuracy.
On the Commitment Capacity of Reverse Elastic Channels

In this work, we study commitment over a class of channels called reverse elastic channels (RECs). In the commitment problem, two mutually distrustful parties, say Alice and Bob, seek to commit on a bit string available to Alice. The parties interact via a commitment protocol comprising two phases, viz., commit phase followed by reveal phase. Alice commits to a string, and transmits it to Bob securely in a manner Bob cannot learn it until Alice chooses to reveal it; at the time of reveal, however, Bob can successfully detect if Alice cheats. It is well known that noisy channels are a promising resource to realize information-theoretically secure commitment; however, oftentimes, channel behaviour may be poorly characterized thereby limiting the commitment throughput and/or degrading the security guarantees. Particularly problematic is a scenario where dishonest parties can actively alter the channel characteristics. RECs are an interesting class of such unreliable channels, where essentially only a dishonest committer Alice can meaningfully alter the channel; RECs have attracted active recent interest. Our principal contribution is the REC commitment capacity characterization for all parameters; this proves a recent related conjecture. Apart from presenting an achievable scheme, a key result in our work is a tight converse which analyses a specific cheating strategy by Alice. The significance of RECs stems from the fact that along with elastic channels (ECs), where only a dishonest receiver Bob can alter the channel, these two channel models represent special cases of the more widely studied unfair noisy channels (UNCs). Interestingly, for a given set of parameters, our result shows that the REC commitment capacity is no larger than that for the ECs. Furthermore, similar to the ECs, RECs offer non-trivial commitment throughput for all meaningful parameters; this is in stark contrast to UNCs where the throughput may possibly be zero.
Semi-Source Independent Quantum Walk Random Number Generation

Semi-source independent quantum random number generators (SI-QRNG) are cryptographic protocols which attempt to extract random strings from quantum sources where the source is under the control of an adversary (but with known dimension) while the measurement devices are fully characterized. This represents a middle-ground between fully-trusted and full-device independence, allowing for fast bit-generation rates with current-day technology, while also providing a strong security guarantee. In this paper we analyze a SI-QRNG protocol based on quantum walks and develop a proof of security. We derive a novel entropic uncertainty relation for this application which is necessary since standard relations actually fail in this case.
A Reconfigurable Accelerator Design for Quantized Depthwise Separable Convolutions

Convolutional Neural Networks (CNNs) are widely applied in various computer version applications such as object recognition and image classification. With large amount of Multiply Accumulate Operations (MACs) in CNN computations, it is a trend to process the data locally at edge devices to avoid significant data movement. MobileNets were proposed as a light-weight neural network for mobile and embedded devices by using depthwise separable convolutions. Moreover, to further reduce commutation efforts, quantization can be appropriately applied to MobileNets without significant accuracy loss. To provide an efficient computation platform for quantized MobileNets, in this paper we propose a novel performance-aware reconfigurable accelerator design which prefect fit the depthwise separable convolutions. Experimental results show that we can achieve 16% area reduction and 1.07x speedup compared to previous MobileNets accelerator design.
The Impact of 3D Finite-Fault Information on Ground-Motion Forecasting for Earthquake Early Warning

ABSTRACT We identify aspects of finite-source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite-fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D-source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line-source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D-source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground-motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.
Enhanced DQN Framework for Selecting Actions and Updating Replay Memory Considering Massive Non-Executable Actions

A Deep-Q-Network (DQN) controls a virtual agent as the level of a player using only screenshots as inputs. Replay memory selects a limited number of experience replays according to an arbitrary batch size and updates them using the associated Q-function. Hence, relatively fewer experience replays of different states are utilized when the number of states is fixed and the state of the randomly selected transitions becomes identical or similar. The DQN may not be applicable in some environments where it is necessary to perform the learning process using more experience replays than is required by the limited batch size. In addition, because it is unknown whether each action can be executed, a problem of an increasing amount of repetitive learning occurs as more non-executable actions are selected. In this study, an enhanced DQN framework is proposed to resolve the batch size problem and reduce the learning time of a DQN in an environment with numerous non-executable actions. In the proposed framework, non-executable actions are filtered to reduce the number of selectable actions to identify the optimal action for the current state. The proposed method was validated in Gomoku, a strategy board game, in which the application of a traditional DQN would be difficult.
Unconstrained face sketch synthesis via perception-adaptive network and a new benchmark

Face sketch generation has attracted much attention in the field of visual computing. However, existing methods either are limited to constrained conditions or heavily rely on various preprocessing steps to deal with in-the-wild cases. In this paper, we argue that accurately perceiving facial region and facial components is crucial for unconstrained sketch synthesis. To this end, we propose a novel Perception-Adaptive Network (PANet), which can generate high-quality face sketches under unconstrained conditions in an end-to-end scheme. Specifically, our PANet is composed of: i) a Fully Convolutional Encoder for hierarchical feature extraction, ii) a Face-Adaptive Perceiving Decoder for extracting potential facial region and handling face variations, and iii) a Component-Adaptive Perceiving Module for facial component aware feature representation learning. To facilitate further researches of unconstrained face sketch synthesis, we introduce a new benchmark termed WildSketch, which contains 800 pairs of face photo-sketch with large variations in pose, expression, ethnic origin, background, and illumination. Extensive experiments demonstrate that the proposed method is capable of achieving state-of-the-art performance under both constrained and unconstrained conditions. Our source codes and the WildSketch benchmark are resealed on the project page http://lingboliu.com/unconstrained_face_sketch.html.
Artificial (or) Fake Human Face Generator using Generative Adversarial Network (GAN) Machine Learning Model

Graphics algorithms for high quality image rendering are highly involved process, as layout, components, and light transport must be explicitly simulated. While existing algorithms excel in this task, creating and formatting virtual environments is a costly and time-consuming process. Thus, there is an opportunity for automating this labor intensive process by leveraging recent development in computer vision. Recent development in deep generative models, especially GANs, has spurred much interest in the computer vision domain for synthesizing realistic images. GANs combine backpropagation with a competitive process involving a pair networks, called Generative Network G and Discriminative Network D, in which G generate artificial images and D classifies it into real or artificial image categories. As the training proceeds, G learns to generate realistic images to confuse D [1]. In this work, a convolutional architecture based on GAN, specifically Deep Convolutional Generative Adversarial Networks (DCGAN) has been implemented to train a generative model that can produce good quality images of human faces at scale. CelebFaces Attributes Dataset (CelebA) has been used to train the DCGAN model. Structural Similarity Index (SSIM), that measures the structural and contextual similarity of two images, has been used for quantitative evaluation of the trained DCGAN model. Obtained results shows that the quality of generated images is quite similar to the high quality images of the CelebA dataset.
Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural model

In this paper we present our 2nd place solution to ACM RecSys 2021 Challenge organized by Twitter. The challenge aims to predict user engagement for a set of tweets, offering an exceptionally large data set of 1 billion data points sampled from over four weeks of real Twitter interactions. Each data point contains multiple sources of information, such as tweet text along with engagement features, user features, and tweet features. The challenge brings the problem close to a real production environment by introducing strict latency constraints in the model evaluation phase: the average inference time for single tweet engagement prediction is limited to 6ms on a single CPU core with 64GB memory. Our proposed model relies on extensive feature engineering performed with methods such as the Efficient Manifold Density Estimator (EMDE) - our previously introduced algorithm based on Locality Sensitive Hashing method, and novel Fourier Feature Encoding, among others. In total, we create numerous features describing user twitter account status and content of a tweet. In order to adhere to the strict latency constraints, the underlying model is a simple residual feed-forward neural network. The system is a variation of our previous methods which proved successful in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We release the source code at: https://github.com/Synerise/recsys-challenge-2021.
Principles of securing RESTful API web services developed with python frameworks

Abstract This article discusses the key points of developing a secure RESTful web service API for keeping a student achievement journal. The relevance of using web services has been analyzed. The classification of web applications is given. The features of the Single Page Application architecture were considered. Comparative characteristics of architectural styles of application programming interfaces are given. Requirements to be met by RESTful API services are considered. The basic principles of API security were analyzed. A list of the main vulnerabilities that may appear during the development of the REST API is given. An overview of popular authentication schemes (methods) is given. Comparative characteristics of web frameworks of the Python programming language are given. The main tools used in the development of web API applications are listed. The process of creating a secure prototype of a RESTful web service API in Python using the Flask microframework and a tool for describing the Swagger specifications is presented. The process of configuring the application was examined in detail. The main recommendations for securing a web application, database and web server settings are listed. The key points of ensuring the protection of the developed web application are considered. The results obtained were analyzed.
MagRing-SASB: Static Authentication of Magnetism Sensor Using Semi-Biometric Interaction Magnetic Ring

The Smartphone is one of the most well-known and widely used devices in contemporary times for protecting private information. Biometric methods are one way for authenticating Smartphones. However, the difference in ages (i.e., elders and children) affects the authentication procedure. This paper describes the current implementation of this technique on a smartphone device, which is in catch utilizing a properly valued magnet intensity (a magnetic ring) taken as a semi-biometric based on static authentication, called MagRing-SBSA. It bases on manipulating the Magnetometer sensor included in the latest generation of mobile devices. It can demonstrate the authentication procedure utilizing a magnetic ring from a magnetometer, detecting data from the unique per device generated in the area surrounding the device by a magnet held in hand. The magnetism applies to various users, and the magnetization ring value can provide a comprehensive authentication option, and it cannot readily duplicate because the ring is unique to the user on a particular smartphone. As a result of the experiments for three participants, two older participants and one child, show a high accuracy rate of 97% because they had undergone training before conducting the experiments. In future work, we will increase participants' numbers and study the effects of using this technique.