Reviewerly/exHarmony huggingface dataset:
Image encryption using finite-precision error

Chaotic systems are broadly adopted to generate pseudo-random numbers used in encryption schemes. However, when implemented on a finite precision computer, chaotic systems end up in dynamical degradation of chaotic properties. Many works have been proposed to address this issue. Nevertheless, little attention has been paid to exploit the finite precision as a source of randomness rather a feature that should be mitigated. This paper proposes a novel plain-image encryption using finite-precision error. The error is obtained by means of the implementation of a chaotic system using two natural different interval extensions. The generated sequence has passed all NIST test, which means it has sufficient randomness to be used in encryption. Several benchmark images have been effectively encrypted using the proposed approach.
Discovering Fair Representations in the Data Domain

Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a job interview based on application materials that may include photographs. One promising direction to achieve fairness is by learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair outcomes. All available models however learn latent embeddings which comes at the cost of being uninterpretable. We propose to cast this problem as data-to-data translation, i.e. learning a mapping from an input domain to a fair target domain, where a fairness definition is being enforced. Here the data domain can be images, or any tabular data representation. This task would be straightforward if we had fair target data available, but this is not the case. To overcome this, we learn a highly unconstrained mapping by exploiting statistics of residuals -- the difference between input data and its translated version -- and the protected characteristics. When applied to the CelebA dataset of face images with gender attribute as the protected characteristic, our model enforces equality of opportunity by adjusting the eyes and lips regions. Intriguingly, on the same dataset we arrive at similar conclusions when using semantic attribute representations of images for translation. On face images of the recent DiF dataset, with the same gender attribute, our method adjusts nose regions. In the Adult income dataset, also with protected gender attribute, our model achieves equality of opportunity by, among others, obfuscating the wife and husband relationship. Analyzing those systematic changes will allow us to scrutinize the interplay of fairness criterion, chosen protected characteristics, and prediction performance.
Multi-label transfer learning for the early diagnosis of breast cancer

Early diagnosis of breast cancer, when it is small and has not spread, can make the disease easier to treat which increases the patient's chances of survival. The recent proposed methods for the early diagnosis of breast cancer, and while showing great success in achieving this goal, rely on one of the indicators in the mammogram to diagnose the patient's condition. Whether it is identifying differences in shapes and patterns of the findings (i.e. masses, calcifications,…etc.) or assessing the breast density as a risk indicator, these Computer-aided Diagnosis (CAD) systems by using single-label classification, fail to exploit the intrinsic useful correlation information among data from correlated domains. Rather than learning to identify the disease based on one of the indicators, we propose the joint learning of the tasks using multi-label image classification. Furthermore, we introduce a new fine-tuning strategy for using transfer learning, that takes advantage of the end-to-end image representation learning when adapting the pre-trained Convolutional Neural Network (CNN) to the new task. We also propose a customized label decision scheme, adapted to this problem, which estimates the optimal confidence for each visual concept. We demonstrate the effectiveness of our approach on four benchmark datasets, CBIS-DDSM, BCDR, INBreast and MIAS, obtaining better results compared to other commonly used baselines.
Exact String Matching Algorithms: Survey, Issues, and Future Research Directions

String matching has been an extensively studied research domain in the past two decades due to its various applications in the fields of text, image, signal, and speech processing. As a result, choosing an appropriate string matching algorithm for current applications and addressing challenges is difficult. Understanding different string matching approaches (such as exact string matching and approximate string matching algorithms), integrating several algorithms, and modifying algorithms to address related issues are also difficult. This paper presents a survey on single-pattern exact string matching algorithms. The main purpose of this survey is to propose new classification, identify new directions and highlight the possible challenges, current trends, and future works in the area of string matching algorithms with a core focus on exact string matching algorithms.
Identifying IoT devices and events based on packet length from encrypted traffic

Recently, machine learning algorithms have been used to identify Internet of Things (IoT) devices and events. However, existing proposals may inspect the packet payload, what creates risks to IoT users’ privacy, and may use several features, increasing the computational complexity for traffic classification. In addition, existing techniques may also use complex mechanisms for extracting traffic characteristics, including the creation of vectors containing data from the Transmission Control Protocol (TCP) sessions. This paper proposes a solution that uses packet length statistics from encrypted traffic to characterize the behavior of IoT devices and events in a smart home scenario. The solution uses only the statistical mean, the standard deviation and the number of bytes transmitted over a one-second window, which can be extracted from the encrypted traffic, making the use of TCP vectors unnecessary. The solution identifies IoT devices and events, such as voice commands to smart assistants, and also distinguishes between IoT and non-IoT devices. The solution to characterize IoT devices and events is evaluated with traffic from two real-world testbeds and five classifiers. The evaluation included the algorithms k-Nearest Neighbors (k-NN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Majority Voting, some of the most popular algorithms for traffic classification. The results show that the Random Forest algorithm can achieve up to 96% of accuracy in the identification of devices, 99% of precision in distinguishing between IoT and non-IoT devices and 99% of accuracy in the identification of IoT device events. Hypothesis testing is used to validate the obtained results. Also, the results show that the Decision Tree presented the lowest latency among the five classifiers evaluated in the identification of the devices, followed by k-NN, Random Forest, SVM and Majority Voting.
Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments

In a cloud environment, the aim of using optimal resources can be achieved using a load-balancing technique. The load-balancing technique assigns a set of requests into a set of resources for distributing the load. It is one of the significant issues in cloud computing and known as an NP-hard problem. Therefore, many nature-inspired meta-heuristic techniques are proposed to provide high efficiency. However, despite the importance of the nature-inspired meta-heuristic techniques for solving the problem of the load-balancing in the cloud environment, there is not a complete and detailed paper about reviewing and studying the main important issues in this domain. Therefore, this paper presents comprehensive coverage of the nature-inspired meta-heuristic techniques applied in the area of the cloud load-balancing. The main goal of this paper is to highlight the emphasis on optimization algorithms and the benefits that they provide to overcome the cloud load-balancing challenges. In addition, to solve the load-balancing problem in the cloud environments, the advantages and disadvantage of the nature-inspired meta-heuristic algorithms have been analyzed and their significant challenges are considered for proposing the techniques that are more effective in the future.
Managing Bias in AI

Recent awareness of the impacts of bias in AI algorithms raises the risk for companies to deploy such algorithms, especially because the algorithms may not be explainable in the same way that non-AI algorithms are. Even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly because AI systems learn from historical data, which encodes historical biases. In this paper, we propose a set of processes that companies can use to mitigate and manage three general classes of bias: those related to mapping the business intent into the AI implementation, those that arise due to the distribution of samples used for training, and those that are present in individual input samples. While there may be no simple or complete solution to this issue, best practices can be used to reduce the effects of bias on algorithmic outcomes.
A deep Coarse-to-Fine network for head pose estimation from synthetic data

Various applications of human-computer interaction are based on the estimation of head pose, which is challenging due to different facial appearance, inhomogeneous illumination, partial occlusion, etc. In this paper, we propose a deep neural network following the Coarse-to-Fine strategy to estimate head poses. The scheme includes two branches: Coarse classification phase classifying the input image into four categories, and Fine Regression phase estimating the accurate pose parameters. The two sub-networks are trained jointly. To tackle the problem of insufficient annotated data in training process, we design a rendering pipeline to synthesize realistic head images and generate an annotated dataset with a collection of 310k head poses. The results on benchmark datasets and synthetic dataset validate the effectiveness of our approach, as well as the results on images with diverse illumination, occlusion, and motion blur. Moreover, our method can be easily extended to estimate head poses on depth images.
Convolutional Neural Network-Based Real-Time Object Detection and Tracking for Parrot AR Drone 2

Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterized particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, parcel delivery (recently started by Amazon), and many more. The sensitivity in performing said tasks demands that drones must be efficient and reliable. For this, in this paper, an approach to detect and track the target object, moving or still, for a drone is presented. The Parrot AR Drone 2 is used for this application. Convolutional Neural Network (CNN) is used for object detection and target tracking. The object detection results show that CNN detects and classifies object with a high level of accuracy (98%). For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected object without losing it from sight. The calculations based on several iterations exhibit that the efficiency achieved for target tracking is 96.5%.
Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification

This work studies the unsupervised re-ranking procedure for object retrieval and person re-identification with a specific concentration on an ensemble of multiple metrics (or similarities). While the re-ranking step is involved by running a diffusion process on the underlying data manifolds, the fusion step can leverage the complementarity of multiple metrics. We give a comprehensive summary of existing fusion with diffusion strategies, and systematically analyze their pros and cons. Based on the analysis, we propose a unified yet robust algorithm which inherits their advantages and discards their disadvantages. Hence, we call it Unified Ensemble Diffusion (UED). More interestingly, we derive that the inherited properties indeed stem from a theoretical framework, where the relevant works can be elegantly summarized as special cases of UED by imposing additional constraints on the objective function and varying the solver of similarity propagation. Extensive experiments with 3D shape retrieval, image retrieval and person re-identification demonstrate that the proposed framework outperforms the state of the arts, and at the same time suggest that re-ranking via metric fusion is a promising tool to further improve the retrieval performance of existing algorithms.
Re-Embodiment and Co-Embodiment

Interactions with multiple conversational agents and social robots are becoming increasingly common. This raises new design challenges: Should agents and robots be modeled after humans, presenting their entity (i.e., social presence) as bound to a single body, or should they take advantage of non-human capabilities, such as moving their social presence from body to body across service touchpoints and contexts? We conducted a User Enactments study in which participants interacted with agents that had one social presence per body, that could re-embody (move their social presence from body to body), and that could co-embody (move their social presence into a body that already contains another). Reactions showed that participants felt comfortable with re-embodying agents, who created more seamless and efficient experiences. Yet situations that required expertise or concentration raised concerns about non-human behaviors. We report on our insights regarding collaboration and coordination with several agents in multi-step interactions.
Optimized Schoolbook Polynomial Multiplication for Compact Lattice-Based Cryptography on FPGA

Lattice-based cryptography (LBC) is one of the most promising classes of post-quantum cryptography (PQC) that is being considered for standardization. This brief proposes an optimized schoolbook polynomial multiplication (SPM) for compact LBC. We exploit the symmetric nature of Gaussian noise for bit reduction. Additionally, a single field-programmable gate array (FPGA) DSP block is used for two parallel multiplication operations per clock cycle. These optimizations enable a significant 2.2× speedup along with reduced resources for dimension n = 256. The overall efficiency (throughput per slice) is 1.28× higher than the conventional SPM, as well as contributing to a more compact LBC system compared to previously reported designs. The results targeting the FPGA platform show that the proposed design can achieve high hardware efficiency with reduced hardware area costs.
Fault-Tolerant Thresholds for the Surface Code in Excess of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mn>5</mml:mn><mml:mo>%</mml:mo></mml:math> Under Biased Noise

Noise in quantum computing is countered with quantum error correction. Achieving optimal performance will require tailoring codes and decoding algorithms to account for features of realistic noise, such as the common situation where the noise is biased towards dephasing. Here we introduce an efficient high-threshold decoder for a noise-tailored surface code based on minimum-weight perfect matching. The decoder exploits the symmetries of its syndrome under the action of biased noise and generalizes to the fault-tolerant regime where measurements are unreliable. Using this decoder, we obtain fault-tolerant thresholds in excess of 6% for a phenomenological noise model in the limit where dephasing dominates. These gains persist even for modest noise biases: we find a threshold of ∼5% in an experimentally relevant regime where dephasing errors occur at a rate 100 times greater than bit-flip errors.
Blockchains and the Supply Chain: Findings from a Broad Study of Practitioners

Blockchain is an emergent technology that has attracted practitioner attention in the supply chain domain. Multiple motivators lead companies to incorporate blockchain technology into their supply chain. However, a number of barriers and challenges may impede the successful adoption of blockchains. The purpose of this paper is to explore how a variety of motivators and barriers are perceived by different companies from different industries. This paper summarizes survey data gathered from 173 respondents associated with the association of supply chain management, formerly APICS. Summary statistics on blockchain adoption by various types of companies are provided to help practitioners benchmark current practice. The paper presents preliminary findings on these dimensions with some insights provided.
Connecting rural schools to quality education: Rural teachers’ use of digital educational resources

Maintaining teaching quality at a high level in rural and remote areas in China can be supported by the use of digital educational resources. This study examined which factors explain differences in rural teachers' use of digital educational resources in their teaching practice in Western China. Data were collected from 462 teachers from 25 primary and secondary schools in rural areas via a questionnaire to gather information about teachers' use of digital educational resources, and school- and teacher-level factors that might influence this. Although various digital educational resources were utilized, electronic lesson plans and multimedia courseware played a dominant role in delivery of lessons. Results from a multilevel regression analysis revealed that no school-level factors seem to be associated with the use of digital educational resources. In contrast, at the teacher level, higher levels of attitudes, knowledge and skills, better facilitating conditions, and teachers' age and teaching experience significantly explained teachers’ use of digital educational resources. However, other key factors such as the intention to use, self-efficacy, and subjective norm did not explain differences in use in the rural school context. The article concludes with some practical implications and recommendations for further research.
Popularity Bias in Ranking and Recommendation

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the "long tail" is critical for businesses as they are less likely to be discovered. Popularity bias is also against social justice where the entities need to have a fair chance of being served and represented. In this work, I investigate the problem of popularity bias in recommender systems and develop algorithms to address this problem.
An Efficient and Safe Road Condition Monitoring Authentication Scheme Based on Fog Computing

In recent years, with the development of intelligent vehicles and wireless sensor network technology, the research on road safety has attracted much attention in vehicular ad-hoc networks (VANETs). By sensing events on the road, vehicles can broadcast information to inform others of traffic jams or accidents. However, the mobile vehicle network has a large transmission delay, which makes real-time content transmission impossible. In this paper, a new certificateless aggregate signcryption scheme (CLASC) is proposed by using a fog computing framework that supports mobility, low latency, and location awareness. It is combined with online/offline encryption (OOE) technology, which reduces many time-consuming operations and improves the security of vehicle users and the efficiency of message authentication. In addition, the scheme has the characteristics of mutual authentication, anonymity, untraceability, and nondeniability. Based on the difficulty of the discrete logarithm problem (DLP) and the computational Diffie-Hellman (CDH) problem, the scheme is further proved to be unforgeability and confidentiality under the random oracle model. The simulation results show that compared with the existing schemes, this scheme can not only ensure the security requirements of the system but also achieve higher efficiency in computing and communication.
The Engineering of Software-Defined Quantum Key Distribution Networks

Quantum computers will change the cryptographic panorama. A technology once believed to lay far away into the future is increasingly closer to real world applications. Quantum computers will break the algorithms used in our public key infrastructure and in our key exchange protocols, forcing a complete retooling of the cryptography as we know it. Quantum Key distribution is a physical layer technology immune to quantum or classical computational threats. However, it requires a physical substrate, and optical fiber has been the usual choice. Most of the time used just as a point to point link for the exclusive transport of the delicate quantum signals. Its integration in a real-world shared network has not been attempted so far. Here we show how the new programmable software network architectures, together with specially designed quantum systems can be used to produce a network that integrates classical and quantum communications, including management, in a single, production-level infrastructure. The network can also incorporate new quantum-safe algorithms and use the existing security protocols, thus bridging the gap between today's network security and the quantum-safe network of the future. This can be done in an evolutionary way, without zero-day migrations and the corresponding upfront costs. We also present how the technologies have been deployed in practice using a production network.
A real-time object detection algorithm for video

Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video.