Reviewerly/exHarmony huggingface dataset:
Data Privacy for a $\rho$ -Recoverable Function

A user's data is represented by a finite-valued random variable. Given a function of the data, a querier is required to recover, with at least a prescribed probability, the value of the function based on a query response provided by the user. The user devises the query response, subject to the recoverability requirement, so as to maximize privacy of the data from the querier. Privacy is measured by the probability of error incurred by the querier in estimating the data from the query response. We analyze single and multiple independent query responses, with each response satisfying the recoverability requirement, which provide maximum privacy to the user. In the former setting, we also consider privacy for a predicate of the user's data. Achievability schemes with explicit randomization mechanisms for query responses are given and their privacy compared with converse upper bounds.
Beyond Weakly Supervised: Pseudo Ground Truths Mining for Missing Bounding-Boxes Object Detection

Due to the shortcomings of the weakly supervised and fully supervised object detection (i.e., unsatisfactory performance and expensive annotations, respectively), leveraging partially labeled images in a cost-effective way to train an object detector has attracted much attention. In this paper, we formulate this challenging task as a missing bounding-boxes' object detection problem. Specifically, we develop a pseudo ground truth mining procedure to automatically find the missing bounding boxes for the unlabeled instances, called pseudo ground truths here, in the training data, and then combine the mined pseudo ground truths and the labeled annotations to train a fully supervised object detector. Furthermore, we propose an incremental learning framework to gradually incorporate the results of the trained fully supervised detector to improve the performance of the missing bounding-boxes' object detection. More importantly, we find an effective way to label the massive images with limited labors and funds, which is crucial when building a large-scale weakly/webly labeled dataset for object detection. The extensive experiments on the PASCAL VOC and COCO benchmarks demonstrate that our proposed method can narrow the gap between the fully supervised and weakly supervised object detectors, and outperform the previous state-of-the-art weakly supervised detectors by a large margin (more than 3% mAP absolutely) when the missing rate equals 0.9. Moreover, our proposed method with 30% missing bounding-box annotations can achieve comparable performance to some fully supervised detectors.
Is There a Social Life in Open Data? The Case of Open Data Practices in Educational Technology Research

In the landscape of Open Science, Open Data (OD) plays a crucial role as data are one of the most basic components of research, despite their diverse formats across scientific disciplines. Opening up data is a recent concern for policy makers and researchers, as the basis for good Open Science practices. The common factor underlying these new practices—the relevance of promoting Open Data circulation and reuse—is mostly a social form of knowledge sharing and construction. However, while data sharing is being strongly promoted by policy making and is becoming a frequent practice in some disciplinary fields, Open Data sharing is much less developed in Social Sciences and in educational research. In this study, practices of OD publication and sharing in the field of Educational Technology are explored. The aim is to investigate Open Data sharing in a selection of Open Data repositories, as well as in the academic social network site ResearchGate. The 23 Open Datasets selected across five OD platforms were analysed in terms of (a) the metrics offered by the platforms and the affordances for social activity; (b) the type of OD published; (c) the FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles compliance; and (d) the extent of presence and related social activity on ResearchGate. The results show a very low social activity in the platforms and very few correspondences in ResearchGate that highlight a limited social life surrounding Open Datasets. Future research perspectives as well as limitations of the study are interpreted in the discussion.
Exploiting Multi-layer Features Using a CNN-RNN Approach for RGB-D Object Recognition

This paper proposes an approach for RGB-D object recognition by integrating a CNN model with recursive neural networks. It first employs a pre-trained CNN model as the underlying feature extractor to get visual features at different layers for RGB and depth modalities. Then, a deep recursive model is applied to map these features into high-level representations. Finally, multi-level information is fused to produce a strong global representation of the entire object image. In order to utilize the CNN model trained on large-scale RGB datasets for depth domain, depth images are converted to a representation similar to RGB images. Experimental results on the Washington RGB-D Object dataset show that the proposed approach outperforms previous approaches.
An Efficient Preconditioner for Stochastic Gradient Descent Optimization of Image Registration

Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems. In the case of badly scaled problems, SGD, however, only exhibits sublinear convergence properties. In this paper, we propose an efficient preconditioner estimation method to improve the convergence rate of SGD. Based on the observed distribution of voxel displacements in the registration, we estimate the diagonal entries of a preconditioning matrix, thus rescaling the optimization cost function. The preconditioner is efficient to compute and employ and can be used for mono-modal as well as multi-modal cost functions, in combination with different transformation models, such as the rigid, the affine, and the B-spline model. Experiments on different clinical datasets show that the proposed method, indeed, improves the convergence rate compared with SGD with speedups around 2~5 in all tested settings while retaining the same level of registration accuracy.
Cache Misses and the Recovery of the Full AES 256 Key

The CPU cache is a hardware element that leaks significant information about the software running on the CPU. Particularly, any application performing sequences of memory access that depend on sensitive information, such as private keys, is susceptible to suffer a cache attack, which would reveal this information. In most cases, side-channel cache attacks do not require any specific permission and just need access to a shared cache. This fact, combined with the spread of cloud computing, where the infrastructure is shared between different customers, has made these attacks quite popular. Traditionally, cache attacks against AES use the information about the victim to access an address. In contrast, we show that using non-access provides much more information and demonstrate that the power of cache attacks has been underestimated during these last years. This novel approach is applicable to existing attacks: Prime+Probe, Flush+Reload, Flush+Flush and Prime+Abort. In all cases, using cache misses as source of information, we could retrieve the 128-bit AES key with a reduction in the number of samples of between 93% and 98% compared to the traditional approach. Further, this attack was adapted and extended in what we call the encryption-by-decryption cache attack (EBD), to obtain a 256-bit AES key. In the best scenario, our approach obtained the 256 bits of the key of the OpenSSL AES T-table-based implementation using fewer than 10,000 samples, i.e., 135 milliseconds, proving that AES-256 is only about three times more complex to attack than AES-128 via cache attacks. Additionally, the proposed approach was successfully tested in a cross-VM scenario.
Staged Detection–Identification Framework for Cell Nuclei in Histopathology Images

Histopathology image is an important basis for pathologists to evaluate disease at the cellular level and colon cancer tissue sections usually contain many different types of nuclei, which should be automatically detected and identified. However, the detection and the identification of cell nuclei are challenging tasks due to the complex tissue structure and the diversity of nuclear morphology. In this paper, a staged detection-identification framework is proposed for cell nuclei in colon cancer histopathology images. First, nuclei positions are detected by a position of interest network, which encodes context-aware representation on input image and decodes features on proximity map. Meanwhile, a cascade residual fusion block is presented to enhance the detection performance during the decoding process. Second, a multicropping network is developed to identify the detected cell nuclei. For reducing the impact of uncertainty, a multicropping module is designed for effectively capturing contextual feature contents around the center of a nucleus. The proposed detection-identification framework is evaluated on an available colorectal adenocarcinoma images data set, which has 100 images including more than 20 000 marked nuclei. Compared with state-of-the-art methods, the proposed approach demonstrates excellent performance with better prediction scores.
Toward Effective Robot--Child Tutoring

Personalized learning environments have the potential to improve learning outcomes for children in a variety of educational domains, as they can tailor instruction based on the unique learning needs of individuals. Robot tutoring systems can further engage users by leveraging their potential for embodied social interaction and take into account crucial aspects of a learner, such as a student’s motivation in learning. In this article, we demonstrate that motivation in young learners corresponds to observable behaviors when interacting with a robot tutoring system, which, in turn, impact learning outcomes. We first detail a user study involving children interacting one on one with a robot tutoring system over multiple sessions. Based on empirical data, we show that academic motivation stemming from one’s own values or goals as assessed by the Academic Self-Regulation Questionnaire (SRQ-A) correlates to observed suboptimal help-seeking behavior during the initial tutoring session. We then show how an interactive robot that responds intelligently to these observed behaviors in subsequent tutoring sessions can positively impact both student behavior and learning outcomes over time. These results provide empirical evidence for the link between internal motivation, observable behavior, and learning outcomes in the context of robot--child tutoring. We also identified an additional suboptimal behavioral feature within our tutoring environment and demonstrated its relationship to internal factors of motivation, suggesting further opportunities to design robot intervention to enhance learning. We provide insights on the design of robot tutoring systems aimed to deliver effective behavioral intervention during learning interactions for children and present a discussion on the broader challenges currently faced by robot--child tutoring systems.
SAFARI: Automatic Synthesis of Fault-Attack Resistant Block Cipher Implementations

Most cipher implementations are vulnerable to a class of cryptanalytic attacks known as fault injection attacks. To reveal the secret key, these attacks make use of faults induced at specific locations during the execution of the cipher. Countermeasures for fault injection attacks require these vulnerable locations in the implementation to be first identified and then protected. However, both these steps are difficult and error-prone and, hence, it requires considerable expertise to design efficient countermeasures. Incorrect or insufficient application of the countermeasures would cause the implementation to remain vulnerable, while inefficient application of the countermeasures could lead to significant performance penalties to achieve the desired fault-attack resistance. In this paper, we present a novel framework called SAFARI for automatically synthesizing fault-attack resistant implementations of block ciphers. The framework takes as input the security requirements and a high-level specification of the block cipher. It automatically detects the vulnerable locations from the specification, applies an appropriate countermeasure based on the user-specified security requirements, and then synthesizes an efficient, fault-attack protected, RTL, or C code for the cipher. We take AES, CAMELLIA, and CLEFIA as case studies and demonstrate how the framework would explore different countermeasures, based on the vulnerability of the locations, the output format, and the required security margins. We then evaluate the efficacy of SAFARI in hardware and software to the design overhead incurred and the fault coverage.
Scalable digit-serial processor array architecture for finite field division

This paper presents a novel scalable digit-serial processor array structure to perform division based on the Stein's division algorithm. The proposed processor array structure enables us to control the number of processing elements as well as the latency of the division operation. This makes us have more flexibility to control the area complexity and the time performance of the divider compared to the previously reported designs. Thus, the presented processor array structure can achieve the time performance requirement of a certain application with minimum hardware resources. The obtained results of the presented design and the previously published competitors' designs indicate that the presented design has a lower area-time product, for bus widths greater than or equal to 26-bits, by percentages ranging from 1.6% to 99% and has a lower energy consumption, for bus widths greater than or equal to 24-bits, by percentages ranging from 4.8% to 99.4% of the compared competitor designs.
A bio-inspired motion sensitive model and its application to estimating human gaze positions under classified driving conditions

Human visual attention performs the spatio-temporal regularity, an inherent regularity in dynamic vision, for the natural visual tasks. However, recent computational visual attention models still have deficiency to reflect the spatio-temporal regularity thoroughly. Motivated by this, we propose a bio-inspired motion sensitive model to estimate human gaze positions in driving. The proposed model has four key advantages. First, inspired by two types of motion sensitive neurons, this model can perceive the motion cues from different directions spatially and temporally. Second, compared to conventional deep learning based models, this model does not rely on expensive training samples with gaze annotations. Third, the proposed model is based upon the visual signal processing without constructing a complex deep neural network architecture, which enables this model to be implemented with low-cost hardware. Fourth, inspired by the visual pathway in drosophila motion vision, the visual signal processing on directional and depth motion sensitive map largely enhances this model's competence in a similar way of human gaze positions in driving. To test this proposed model, we collect a driving scene dataset from the perspective of egocentric vision that aims to systematically evaluate the performance of spatio-temporal visual attentional models. The video clips in the dataset are categorized into ten popular driving conditions. The proposed model is evaluated by comparing with the human baseline of the gaze positions. Experimental results demonstrate that the proposed model can effectively estimate the human gaze positions in driving and consistently outperforms traditional visual attention models as well as the deep learning based model.
Extraction of actionable knowledge to reduce hospital readmissions through patients personalization

In recent years, healthcare spending has risen and become a burden on governments especially in the US. One of the reasons for this increase is hospital readmissions. Decreasing the number of readmissions can reduce the healthcare spending. The first part of this paper examines state-of-the-art applications that aim to reduce the number of readmissions at hospitals. We categorize these applications into three different categories and highlight not only their benefits, but also their weaknesses. Moreover, we describe the limitations and challenges faced by these systems to demonstrate the possible future research paths. The second part of this paper presents novel algorithms to reduce the number of readmissions by applying the concept of personalization and actionable patterns. First, we start by extracting all possible procedure paths (course of treatments) for a given procedure. Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the next procedure. Finally, we present a novel algorithm that provides recommendations (actionable knowledge) to the physicians to put patients on a treatment path that would result in optimal reduction of the number of readmissions on average case.
Joint Domain Matching and Classification for cross-domain adaptation via ELM

Recent years, domain adaptation has attracted much attention in the community of machine learning. In this paper, we mainly focus on the tasks of Joint Domain Matching and Classification (JDMC) under the framework of extreme learning machine (ELM). Specifically, our JDMC method is formulated by optimizing both the output-adapted transformation and the cross-domain classifier, which allows us to simultaneously (1) align the source domain and target domain in the feature space with correlation alignment, (2) minimize the discrepancy between the source and target domain, measured in terms of both marginal and conditional probability distribution in the mapped feature space, (3) select informative features which behave similarly in both domains for knowledge transfer by imposing ℓ2,1-norm on the output weights of ELM. In this respect, the proposed JDMC integrates the feature matching, feature selection and classifier design in a unified framework. Besides, an efficient alternative optimization strategy is exploited to solve the joint learning model. To evaluate the effectiveness of the proposed method, extensive experiments on several commonly used domain adaptation datasets are presented, the results show that the proposed method significantly outperforms the non-transfer ELM networks and consistently outperforms several state-of-art domain adaptation methods.
A data-efficient deep learning approach for deployable multimodal social robots

The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games---and use the game of `Noughts & Crosses' with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images, a few example multimodal dialogues and physical demonstrations of robot manipulation, and automatic simulations. In addition, we propose novel algorithms for robust visual game tracking and for competitive policy learning with high winning rates, which substantially outperform DQN-based baselines. While an automatic evaluation shows evidence that the proposed approach can be easily extended to new games with competitive robot behaviours, a human evaluation with 130 humans playing with the Pepper robot confirms that highly accurate visual perception is required for successful game play.
Skills Requirements for Cyber Security Professionals: A Content Analysis of Job Descriptions in South Africa

With cybercrime increasing at a rapid rate due to the improvement in technologies, cyber security has become a global matter of interest. Cyber security professionals protect organisations from cybercrime and other cyber threats. A shortage of skills has increased the demand for cyber security professionals. This research study examines the skills, knowledge and qualification requirements that are required of a cyber security professional in South Africa. 196 unique job advertisements from 5 job portal websites were collected. A content analysis of the advertisements show that cyber security professionals require technical and interpersonal skills amongst others. Organisations looking to employ cyber security professionals require most professionals to have a Bachelor degree in either Computer Science, Information Systems or Engineering as a minimum qualification along with industry certifications. This paper is an initial exploratory study that can serve as a basis for more specific studies in future.
REWARDS: Privacy-preserving rewarding and incentive schemes for the smart electricity grid and other loyalty systems

In this work we present REWARDS (pRivacy prEserving reWARDing Scheme), a generic framework to be used for reward collection in the smart grid and other loyalty systems. A useful functionality to motivate user participation in such systems is to provide for a privacy-preserving mechanism to reward users for the electricity data they submit to a utility provider. Our lightweight scheme allows participants to earn and redeem incentives in a way that cannot be linked to their actions or identities. While our presentation focuses around the smart electricity grid, the generic character of REWARDS makes it a perfect candidate for other application domains and loyalty systems where user participation is critical to the success of the application. One such important paradigm is mobile crowd-sensing, where users contribute data sensed with their smart devices to a campaign administrator and expect a reward for them. We have analyzed the security properties of our scheme and showed that our reward tokens are indeed privacy-respecting; rewards are unlinkable to each other and token transactions do not leak any information about the user. Additionally, we have showed that our solution is highly efficient in terms of computation, communication and storage overhead, thus guaranteeing good performance in practice.
Tag2Vec: Learning Tag Representations in Tag Networks

Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks. In real networks, nodes may have multiple tags but existing methods ignore the abundant semantic and hierarchical information of tags. This information is useful to many network applications and usually very stable. In this paper, we propose a tag representation learning model, Tag2Vec, which mixes nodes and tags into a hybrid network. Firstly, for tag networks, we define semantic distance as the proximity between tags and design a novel strategy, parameterized random walk, to generate context with semantic and hierarchical information of tags adaptively. Then, we propose hyperbolic Skip-gram model to express the complex hierarchical structure better with lower output dimensions. We evaluate our model on the NBER U.S. patent dataset and WordNet dataset. The results show that our model can learn tag representations with rich semantic information and it outperforms other baselines.