Reviewerly/exHarmony huggingface dataset:
A Clonal Selection Optimization System for Multiparty Secure Computing

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.
Online Estimation Method of Badminton Players' Grip Position Error Based on Particle Filtering

In order to solve the problems of low accuracy and long time consumption of traditional badminton players' grip position error estimation methods, a new online estimation method of badminton players' grip position error based on particle filter was proposed in this paper. Infrared array sensor is used to collect badminton players' grip posture images, and the collected images are de-noised and segmenting. The probability density of pixel points of the processed images is estimated, and the grip posture area and the contour features of the grip posture are extracted, so as to realize the recognition of badminton players' grip posture. On this basis, the similarity of the grip pose sequence and the standard grip pose sequence were taken as two kinds of particles. By comparing the similarity of the two kinds of particles, the final online estimation result of the grip pose error was obtained. The experimental results show that the maximum error rate of badminton players' grip position recognition is only 3.96%, the error estimation accuracy is between 95.76%–98.25%, and the estimated time is between 0-0.5s.
You’ve Got (a Reset) Mail: A Security Analysis of Email-Based Password Reset Procedures

The password recovery process is a critical part of a website's functionality. Many websites that provide online services to their users also need to solve the problem of allowing their users to reset their passwords (e.g., if they have forgotten it). A popular, established technique for allowing a user to recover a lost account is to allow her to send a reset link to her own account via email. Although it might seem easy at a first glance, the security requirements of the password recovery process require web sites to carefully design each step of the process to be resilient even in the presence of an attack. In this paper, we present an in-depth security analysis of the email-based recovery mechanisms of a wide range of web applications. By manually registering accounts and triggering the password recovery process for each website, we were able to study the password reset mechanisms of web sites from three different groups in the Alexa Top 5K (i.e., popular sites, medium popular sites, low popular sites). In this work, we show that the lack of standards in the password recovery process plagues many websites with security weaknesses, and negatively influences the security of the reset process itself. We also show that concrete password-recovery reset attacks can be launched against a high percentage of websites that might even lead to account takeover.
Use of Affective Visual Information for Summarization of Human-Centric Videos

The increasing volume of user-generated human-centric video content and its applications, such as video retrieval and browsing, require compact representations addressed by the video summarization literature. Current supervised studies formulate video summarization as a sequence-to-sequence learning problem, and the existing solutions often neglect the surge of the human-centric view, which inherently contains affective content. In this study, we investigate the affective-information enriched supervised video summarization task for human-centric videos. First, we train a visual input-driven state-of-the-art continuous emotion recognition model (CER-NET) on the RECOLA dataset to estimate activation and valence attributes. Then, we integrate the estimated emotional attributes and their high-level embeddings from the CER-NET with the visual information to define the proposed affective video summarization (AVSUM) architectures. In addition, we investigate the use of attention to improve the AVSUM architectures and propose two new architectures based on temporal attention (TA-AVSUM-GRU) and spatial attention (SA-AVSUM-GRU). We conduct video summarization experiments on the TvSum and COGNIMUSE datasets. The proposed temporal attention-based TA-AVSUM architecture attains competitive video summarization performances with strong improvements for the human-centric videos compared to the state-of-the-art in terms of F-score, self-defined face recall, and rank correlation metrics.
A Novel Cryptography-Based Privacy-Preserving Decentralized Optimization Paradigm

Existing large-scale optimization schemes are challenged by both scalability and cyber-security. With the favorable scalability, adaptability, and flexibility, decentralized and distributed optimization paradigms are widely adopted in cyber-physical system applications. However, most existing approaches heavily rely on explicit information exchange between agents or between agents and the system operator, leading the entire framework prone to privacy risks. To tackle this issue, this paper synthesizes cryptography and decentralized optimization techniques to develop a novel privacy-preserving decentralized optimization paradigm. The proposed paradigm is generically applicable to strongly coupled convex optimization problems with nonseparable objective functions and linearly coupled constraints. The security and accuracy of the proposed paradigm are verified via numerical examples.
Straddling the Divide: A Framework for Digital Skills Education in Support of Both the UN Sustainable Development Goals and the Fourth Industrial Revolution in South Africa

South Africa has endorsed both the UN Sustainable Development Goals (UN SDG) which is to be aligned to its National Development Plan and the agenda of the Fourth Industrial Revolution (4IR), which will inform economic direction and is supported in the country’s forthcoming Science and Technology White Paper. These two agendas are potentially divergent, and care is required to align the actions taken in support of these agendas. Both ICT and education are core drivers of both agendas and thus the development of human capital through digital skills education has the potential to play a significant role in enhancing both agendas, but if approached inappropriately may contribute to deepening the digital divide in the country. This paper proposes a conceptual framework for digital skills education that would allow human capital development and the development of digital skills that would enable digital support for both the SDG and 4IR agendas. The paper provides an example of a South African digital skills training programme undertaken on behalf of the South African government by the National Electronic Media Institute of South Africa (NEMISA) as an example of the rollout of digital skills to an African nation. The paper finally indicates the implications of the proposed conceptual educational framework for the provision of digital skills in South Africa.
Case Study on Prototyping Educational Applications Using Persona-Based Approach

In the twenty-first century, many technologies and applications have been developed to support the digital learning environment for teachers and students either physically in class or virtually at home. The need has increased due to the current COVID-19 pandemic, as students must join online teaching and learning at home either they are at primary, secondary, or tertiary levels. Since the requirements are different according to the level of education, some educational applications are not suitable and complicated for lower education levels. Considering various users' needs, persona-based approach has been used in many studies to elicit users' requirements and derive diverse scenarios from user personas before moving to implementation stage. Thus, this case study proposes a persona-based approach to identify students' goals or needs, pains or challenges besides their satisfaction when using educational application prototypes. It involved eight prototypes for learning Computer Science topics and 29 secondary school students. The findings conclude how the persona-based approach can be used to investigate diverse users' needs, challenges, and scenarios as the inputs to design educational applications with digital contents or e-contents for specific topics or subjects.
Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates

It is challenging for humans to enable visual knowledge discovery in data with more than 2–3 dimensions with a naked eye. This chapter explores the efficiency of discovering predictive machine learning models interactively using new Elliptic Paired coordinates (EPC) visualizations. It is shown that EPC are capable to visualize multidimensional data and support visual machine learning with preservation of multidimensional information in 2-D. Relative to parallel and radial coordinates, EPC visualization requires only a half of the visual elements for each n–D point. An interactive software system EllipseVis, which is developed in this work, processes high-dimensional datasets, creates EPC visualizations, and produces predictive classification models by discovering dominance rules in EPC. By using interactive and automatic processes it discovers zones in EPC with a high dominance of a single class. The EPC methodology has been successful in discovering non-linear predictive models with high coverage and precision in the computational experiments. This can benefit multiple domains by producing visually appealing dominance rules. This chapter presents results of successful testing the EPC non-linear methodology in experiments using real and simulated data, EPC generalized to the Dynamic Elliptic Paired Coordinates (DEPC), incorporation of the weights of coordinates to optimize the visual discovery, introduction of an alternative EPC design and introduction of the concept of incompact machine learning methodology based on EPC/DEPC.
Towards Semantic-Awareness for Information Management and Planning in Health Dialogues

Dialogue systems for the health domain are expected to be reliable and to reason in accordance to medical experts' reasoning. Given the complexities of the health domain, these systems are frequently aided by semantic-aware approaches implementing technologies such as ontologies. However, the automated generation of such systems is still a challenging task. In this work, we propose an approach that integrates automated planning and information management with the aim of automating the generation of efficient dialogue managers. Resulting dialogue managers are capable of handling multi-turn goal-oriented dialogue sessions within the healthcare domain. By evaluating a prototype on the asthma domain, our results reveal the suitability of our approach to generate dialogue policies on real-time scenarios.
The state-of-the-art Boolean Satisfiability based cryptanalysis

Over the past two decades, the effectiveness of Conflict-Driven Boolean Satisfiability (CDCL SAT) solutions in a range of applications such as verification, monitoring, safety and the AI has improved dramatically in relation to industrial problems. The availability of powerful general-purpose search tools such as the SAT solver have led many scientists to propose satellite methods for cryptanalysis such as havoc collision techniques and symmetric encryption schemes. One feature of all of the above suggested SAT cryptanalysis is that it is a black box in that the problem with encrypted cryptanalysis is encoded as an instance with SAT and a CDCL SAT solver is called upon to solve that instance. A limitation is that the encoding produced may be too large to solve efficiently by any modern solver. Perhaps more importantly, the solver is in no way trained or tailored for solving the particular instance. Finally, very little work has been done in the field of SAT-based cryptanalysis to exploit parallelism.
Multi-Resolution and Multi-Domain Analysis of Off-Road Datasets for Autonomous Driving

For use in off-road autonomous driving applications, we propose and study the use of multi-resolution local binary pattern texture descriptors to improve overall semantic segmentation performance and reduce class imbalance effects in off-road visual datasets. Our experiments, using a challenging publicly available off-road dataset as well as our own off-road dataset, show that texture features provide added flexibility towards reducing class imbalance effects, and that fusing color and texture features can improve segmentation performance. Finally, we demonstrate domain adaptation limitations in nominally similar off-road environments by cross-comparing the segmentation performance of convolutional neural networks trained on both datasets.
Runtime analysis via symmetry arguments

We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of [EQUATION] iterations to find any particular target search point. This bound is valid for all population sizes μ. Our result improves and extends the previous lower bound of Ω(exp(nδ/2)) valid for population sizes μ = O(n1/2--δ), 0 < δ < 1/2. This paper for the Hot-off-the-Press track at GECCO 2021 summarizes the work Benjamin Doerr. Runtime Analysis of Evolutionary Algorithms via Symmetry Arguments. Information Processing Letters, 166:106064. 2021. [5].
Investigating the Robustness and Generalizability of Deep Reinforcement Learning Based Optimal Trade Execution Systems

Recently, researchers from both academia and the financial industry have attempted to leverage the cutting edge techniques of Deep Reinforcement Learning (DRL) to develop autonomous trade execution systems. They desire a system that could learn from the trade data and develop trade execution strategies to optimize the execution costs. While several researchers have reported success in developing such autonomous trade execution systems, none of them have investigated the robustness of the systems. Despite the powerfulness of DRL, the overfitting and generalization remain a challenge in DRL. For real-world applications, especially in financial trading, the DRL system's robustness is critical, as the cost of wrong decisions could be devastating. In our experiment, we investigate the robustness of the DRL based autonomous trade execution systems by applying the policies learned from one stock to other stocks directly without any refining or training. As different stocks have different dynamics, they might serve as suitable environments to investigate the systems' capability to generalize. The result suggests that the DRL systems have generalized well on other stocks without further refining or training on the specific stock's historical trade data. The result is significant from two perspectives: 1) It suggests that the DRL based trade execution systems can generalize well, which gives us and potential users more confidence in their performances; 2) It also presents an opportunity to develop a more sample efficient system.
An Optimization Driven Approach for Designing Touch Sensor Panels for Integration With Antennas

Objective: We present a systematic method of integrating 5G printed antennas with bezel-less capacitive touch sensor panels (TSPs). Need: With these displays, only the space underneath the TSP is available for antenna integration. But, the electrodes of contemporary TSPs are so closely packed that they reflect impinging electromagnetic (EM) waves, distorting the radiation patterns of the antenna placed underneath. Thus, the system requires more power to compensate for these reflections and maintain high quality data transfer. Method: We utilize pattern search optimization to design the touch sensing electrodes as frequency selective surfaces with a pass-band at the operating frequency of the antenna. We carry out the optimization initially at 4.7GHz to demonstrate its advantages with respect to antenna integration. We fabricated the optimized TSP and measured its transmission and reflection coefficients for a normally-incident plane wave. The designed TSP is broadband and has a measured bandwidth of 8.4% at 4.76GHz. We explore the optimization process further for other frequencies in the 5G-NR frequency range 1 spectrum (4GHz, and 5.5GHz) to show its robustness. Touch performance: We evaluate the touch sensing response of the optimized TSP using quasielectrostatic simulations. Results: We designed, optimized and realized a TSP for a 4.7GHz antenna. We integrated the optimized and conventional TSPs with a printed dipole antenna and measured the radiation patterns of the antenna underneath the TSP at 4.7 GHz. The measured radiation pattern for the optimized TSP case was nearly identical to the free-space pattern of the dipole. With our proposed solution, antennas can claim the space underneath the TSP which is paramount for 5G wireless standards that require multiple antennas for intelligent radios.
Home Appliance Control Using Smartwatches with Continuous Gesture Recognition

This work proposes the development of a method to control household appliances using smartwatches with continuous gesture recognition and carries out a pilot study to validate the developed method. The devices are controlled by infrared signals. We developed a prototype for smartwatches that allows the user to control the devices by making simple gestures, for this, we define a set of simple gestures made up of lines and curves, which represent commands to the devices. Thus, the method allows the user to send various commands to devices, such as on/off, increase and decrease temperature, increase and decrease volume, among others. We use the method to control the following devices: air conditioner, TV, video player, audio player and lamp. As we use the continuous gesture recognition algorithm, a gesture can be recognized before the user finishes it, allowing for quick interaction. We use the "Broadlink RM Mini 3", an omnidirectional mini infrared controller to send the infrared signals. After performing a gesture by the user, we use the continuous gesture recognition algorithm to recognize the gestures, then the command code corresponding to the gesture is sent to the "Broadlink RM Mini 3" that emits the infrared signal to be received by the device. The pilot study showed that it was possible to control the devices using the proposed method and that the commands were correctly sent to the devices with satisfactory response time.
Estimating the Damage of Earthquake Using RADIUS Model (Case Study: Tehran)

By its specific geographical and climate conditions, Iran benefits from high environmental and climate variability. Besides abundant relative advantages, this variety has increased the potential of different natural risks. Earthquake, as one of the most significant geotechnical risks, threatens most regions throughout Iranian Plateau. The importance of earthquake risk in Iran has been increased by the increased development of cities and the concentration of population. Tehran, as the most important city based on its population, economic and social infrastructures, and because it has been surrounded by several active faults as well as located on some active ones, exposed to high risk against earthquake risk. In this case, one of the requirements of urban management in Tehran is studying the seismic risks of this town as well as estimating its probable damages. For this reason, region 3 of district 1 of Tehran municipality having many old textures than other districts have been chosen as a region of study. The methodology of this study, data collection, and analysis are conducted by methods based on database benefiting from RADIUS Program, and there have been considered two scenarios based on both faults in the North Tehran as an origin for estimating the risks from the probable earthquake in the region. Results indicate that maximum risk might be considered in the scenario of fault located in North Tehran.
FitFone: Tracking Home Workout in Pandemic Times

Global pandemics, such as COVID-19, significantly impact mental and physical well-being. Regular home workouts become more critical during these extraordinary times, as physical activity can positively impact our health. Having the ability to track workout progress motivates consistent home workout schedules. In this paper, we demonstrate two proof-of-concept implementations enabling the tracking of home workouts. First, we demonstrate exercise tracking when the user wears a smartphone relying on IMU data. Second, we show tracking when a doffed phone is placed in front of the user while relying on inaudible Doppler sensing.
Research and Implementation of Dimension Reduction Algorithm in Big Data Analysis

With the rapid development of computer and Internet technologies, these data are usually high-dimensional, and the huge amount of data information puts a burden on our calculation and information acquisition. Especially in the fields of data analysis and prediction, it is about that for us to obtain the target data efficiently. Big data analysis is a technique for quickly and efficiently extracting information from multiple types of data. The dimensionality reduction algorithm is an important part of it, which can help us quickly extract valuable parts from a huge amount of data and improve the efficiency of calculation. The core idea of the dimensionality reduction algorithm is to use a mapping method to map the data points in the original dimensional space to the low-dimensional space. This article mainly uses dimensionality reduction algorithms to deal with air quality data sets concept of data classification is introduced into the dimensionality reduction algorithm, which improves the accuracy of data dimensionality reduction. On this basis, a logistic regression algorithm is added, boulevard improving the effect of data visualization.