Reviewerly/exHarmony huggingface dataset:
Secure Outsourcing Algorithm of BTC Feature Extraction in Cloud Computing

Advances in cloud computing have aroused many researchers' interest in privacy-preserving feature extraction over outsourced multimedia data, especially private image data. Since block truncation coding (BTC) is known as a simple and efficient technology for image compression, this paper focuses on privacy-preserving feature extraction in BTC compressed domain. We propose a privacy-preserving computation of BTC feature extraction over massive encrypted images (also called PPBTC). First, all images are uploaded to the cloud after encryption. The privacy-preserving image encryption process consists of block permutation, pixel diffusion, and a bit-plane random shift. BTC features remain unchanged after encryption and the cloud server can directly extract BTC features from the encrypted images. Some analyses and experimental results demonstrate that the proposed privacy-preserving feature extraction scheme for BTC-compressed images is efficient and secure, and it can be applied to secure image computation applications in cloud computing.
Discourses of exclusion on Twitter in the Turkish Context: #ülkemdesuriyeliistemiyorum (#idontwantsyriansinmycountry)

The new communicative affordances of online spaces have transformed the ways and domains we build and negotiate meaning. At the same time, they have introduced diverse channels to produce and disseminate animosity. This article explores online discourses as new communicative environments characterized by their unique textual and semiotic features to unfold the discursive constructions of hate and hostility towards Syrian refugees in Turkey. Building on the principles of the Social Media Critical Discourse Studies (SM-CDS) framework proposed by KhosraviNik (2018) and the Discourse-Historical Approach (DHA) by Reisigl and Wodak (2001), the study analyzes a subset of tweets that includes the hashtag #ülkemdesuriyeliistemiyorum (#idontwantsyriansinmycountry) to understand its functions in constructing and proliferating an exclusionary discourse against refugees. The study focuses on referential, argumentation and intensification strategies used in tweets as well as their wider socio-political implications. The results reveal that refugees in Turkey are delineated as threats, invaders, criminals and potential dangers by the users of online media. It is further observed that a sharper rhetoric and a more intense negative-other representation emerge in Twitter as an online public space compared to print media discourses. While scrutinizing the (re)construction and representation of refugees, our analysis has also uncovered that hate and hostility discourses towards refugees constantly operate to build a collective nationalist identity. This interlocking relationship between constructing refugees through stereotypical attributes as a homogeneously dangerous group and forming a collective Turkish identity is manifested at each level of our analysis.
A Developmental Cognitive Architecture for Trust and Theory of Mind in Humanoid Robots

As artificial systems are starting to be widely deployed in real-world settings, it becomes critical to provide them with the ability to discriminate between different informants and to learn from reliable sources. Moreover, equipping an artificial agent to infer beliefs may improve the collaboration between humans and machines in several ways. In this article, we propose a hybrid cognitive architecture, called Thrive, with the purpose of unifying in a computational model recent discoveries regarding the underlying mechanism involved in trust. The model is based on biological observations that confirmed the role of the midbrain in trial-and-error learning, and on developmental studies that indicate how essential is a theory of mind in order to build empathetic trust. Thrive is build on top of an actor-critic framework that is used to stabilize the weights of two self-organizing maps. A Bayesian network embeds prior knowledge into an intrinsic environment, providing a measure of cost that is used to boostrap learning without an external reward signal. Following a developmental robotics approach, we embodied the model in the iCub humanoid robot and we replicated two psychological experiments. The results are in line with real data, and shed some light on the mechanisms involved in trust-based learning in children and robots.
Segmentation of Cell Images Based on Improved Deep Learning Approach

The improved U_net algorithm based on mixed convolution blocks (McbUnet), which combines the advantages of U-Net and residual learning, is proposed for cell image segmentation in this paper.The network is mainly composed of two kinds of mixed convolution blocks.There are three main benefits to this algorithm.First, the convolution block can utilize different size kernels to overcome the limitation of a single size convolution kernel in traditional deep convolution.Second, in the mixed convolution blocks, two hyperparameters (Width multiplier and Resolution multiplier) are used to quickly adjust the model to fit a specific environment.Third, the residual paths are improved.We test the proposed network and compare it with other recent segmentation methods based on deep learning.The proposed method is superior to comparison methods, which shows its effectiveness.
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification

In cross-lingual text classification, one seeks to exploit labeled data from one language to train a text classification model that can then be applied to a completely different language. Recent multilingual representation models have made it much easier to achieve this. Still, there may still be subtle differences between languages that are neglected when doing so. To address this, we present a semi- supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. The resulting model then serves as a teacher to induce labels for unlabeled target lan- guage samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language. Compared with a number of strong baselines, we observe signifi- cant gains in effectiveness on document and intent classification for a diverse set of languages.
Distilling Cross-Task Knowledge via Relationship Matching

The discriminative knowledge from a high-capacity deep neural network (a.k.a. the "teacher") could be distilled to facilitate the learning efficacy of a shallow counterpart (a.k.a. the "student"). This paper deals with a general scenario reusing the knowledge from a cross-task teacher --- two models are targeting non-overlapping label spaces. We emphasize that the comparison ability between instances acts as an essential factor threading knowledge across domains, and propose the RElationship FacIlitated Local cLassifiEr Distillation (ReFilled) approach, which decomposes the knowledge distillation flow into branches for embedding and the top-layer classifier. In particular, different from reconciling the instance-label confidence between models, ReFilled requires the teacher to reweight the hard triplets push forwarded by the student so that the similarity comparison levels between instances are matched. A local embedding-induced classifier from the teacher further supervises the student's classification confidence. ReFilled demonstrates its effectiveness when reusing cross-task models, and also achieves state-of-the-art performance on the standard knowledge distillation benchmarks. The code of the paper can be accessed at https://github.com/njulus/ReFilled.
ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture

The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While freely moving cameras, such as on drones, provide control over this viewpoint, automatically positioning them at the location which will yield the highest accuracy remains an open problem. This is the problem that we address in this paper. Specifically, given a short video sequence, we introduce an algorithm that predicts which viewpoints should be chosen to capture future frames so as to maximize 3D human pose estimation accuracy. The key idea underlying our approach is a method to estimate the uncertainty of the 3D body pose estimates. We integrate several sources of uncertainty, originating from deep learning based regressors and temporal smoothness. Our motion planner yields improved 3D body pose estimates and outperforms or matches existing ones that are based on person following and orbiting.
Estimation of a Function of Low Local Dimensionality by Deep Neural Networks

Deep neural networks (DNNs) achieve impressive results for complicated tasks like object detection on images and speech recognition. Motivated by this practical success, there is now a strong interest in showing good theoretical properties of DNNs. To describe for which tasks DNNs perform well and when they fail, it is a key challenge to understand their performance. The aim of this paper is to contribute to the current statistical theory of DNNs. We apply DNNs on high dimensional data and we show that the least squares regression estimates using DNNs are able to achieve dimensionality reduction in case that the regression function has locally low dimensionality. Consequently, the rate of convergence of the estimate does not depend on its input dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> , but on its local dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d^{*}$ </tex-math></inline-formula> and the DNNs are able to circumvent the curse of dimensionality in case that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d^{*}$ </tex-math></inline-formula> is much smaller than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> . In our simulation study we provide numerical experiments to support our theoretical result and we compare our estimate with other conventional nonparametric regression estimates. The performance of our estimates is also validated in experiments with real data.
IoT Data Security Via Blockchain Technology and Service-Centric Networking

Two of the foremost talked concerning technologies in today’s world are Blockchain and IoT. They're at the initial stage of maturity and there are plenty of activities going down within development of them and finding fascinating uses of the technologies. The data transmitted by IoT devices is very important and must be secure. Based on the IoT security problems, this paper proposed a model for secure IoT data based on Blockchain; the model called service-centric networking (SCN). SCN provides communication by service names instead of addresses, and a decentralized network that allows the users of the network to communicate fast and data are reliable and secure. However, the identity of the users will be an encrypted format that is stored on their device backed by InterPlanetary File System (IPFS).
Combinatorial list-decoding of Reed-Solomon codes beyond the Johnson radius

List-decoding of Reed-Solomon (RS) codes beyond the so called Johnson radius has been one of the main open questions in coding theory and theoretical computer science since the work of Guruswami and Sudan. It is now known by the work of Rudra and Wootters, using techniques from high dimensional probability, that over large enough alphabets there exist RS codes that are indeed list-decodable beyond this radius.
The power of factorization mechanisms in local and central differential privacy

We give new characterizations of the sample complexity of answering linear queries (statistical queries) in the local and central models of differential privacy:
Fuzzy cognitive mapping with Inuit women: what needs to change to improve cervical cancer screening in Nunavik, northern Quebec?

Abstract Background Among Canadian Inuit, cervical cancer incidence and mortality rates are up to three times higher than the Canadian average. Cervical cancer is preventable through regular screening which, in Quebec, is opportunistic and requires physical examination and Papanicolaou (“Pap”) smears. Since Human Papillomavirus (HPV) is the necessary cause of cervical cancer, HPV testing is a plausible screening alternative. HPV testing by self-sampling also addresses several barriers associated with physical examination and access to healthcare. In a participatory research paradigm, we worked with two communities of Nunavik to explore the possible implementation of HPV self-sampling. Method Key community stakeholders formed an Advisory Committee to guide direct discussions with Inuit women. We presented available facts around cervical cancer, HPV and the female anatomy, and used Fuzzy Cognitive Mapping to collate women’s views. A thematic analysis summarized data, adding links and weights to represent the relationship of each factor on the outcome: screening for cervical cancer. Results According to the 27 Inuit women who participated, the most influential factor in using health services was the cultural awareness of the healthcare provider. A significant barrier to screening was patient lack of information. The principal vector of change – the factor most likely to influence other factors – was the means of communication between the healthcare provider and the patient: visual communication was told to be the most effective. Conclusion Fuzzy Cognitive Mapping is a practical tool for discussing possible health actions with stakeholders and to inform future research. The tool offers a visual aid for discussion across cultural and educational differences. It can help to build the partnerships that incorporate community voices into co-design of interventions that are relevant to and aligned with the needs of those who use them.
Sequential-based Adversarial Optimisation for Personalised Top-N Item Recommendation

Personalised top-N item recommendation systems aim to generate a ranked list of interesting items to users based on their interactions (e.g. click, purchase and rating). Recently, various sequential-based factorised approaches have been proposed to exploit deep neural networks to effectively capture the users' dynamic preferences from their sequences of interactions. These factorised approaches usually rely on a pairwise ranking objective such as the Bayesian Personalised Ranking (BPR) for optimisation. However, previous works have shown that optimising factorised approaches with BPR can hinder the generalisation, which can degrade the quality of item recommendations. To address this challenge, we propose a Sequential-based Adversarial Optimisation (SAO) framework that effectively enhances the generalisation of sequential-based factorised approaches. Comprehensive experiments on six public datasets demonstrate the effectiveness of the SAO framework in enhancing the performance of the state-of-the-art sequential-based factorised approach in terms of NDCG by 3-14%.
Multi-Stage Queries and Temporal Scoring in Vitrivr

The increase in multimedia data brings many challenges for retrieval systems, not only in terms of storage and processing requirements but also with respect to query formulation and retrieval models. Querying approaches which work well up to a certain size of a multimedia collection might start to decrease in performance when applied to larger volumes of data. In this paper, we present two extensions made to the retrieval model of the open-source content-based multimedia retrieval stack vitrivr which enable a user to formulate more precise queries which can be evaluated in a staged manner, thereby improving the result quality without sacrificing the system's overall flexibility. Our retrieval model has shown its scalability on V3C1, a video collection encompassing approx. 1000 hours of video.
THE EVOLUTION OF VIRTUAL REALITY TOWARDS THE USAGE IN EARLY DESIGN PHASES

Abstract The aim of the present work is to show possibilities with which 3D models in virtual reality (VR) can be created and exported. First an overview of the existing hard- and software is given. Subsequently, existing solution concepts are analysed which, however, are not used in product development. Based on these knowledge a concept for the creation of 3D models in VR and the export of these models is developed.
Autoregressive Model Based Smoothing Forensics Of Very Short Speech Clips

Smoothing is a post-processing widely used in speech tampering. Thus, we may determine whether a speech signal is original by smoothing forensics. However, in existing smoothing forensics methods, the detection performance of very short speech clips is much worse than that of long speech clips, and MP3 compression may lead to performance degradation, especially when the length of the smoothing window becomes small. Based on the observation that a very short speech clips can be considered as a stationary autoregressive (AR) process model, we proposed a robust smoothing forensics method of very short speech clips using the AR model coefficients. Experimental results on the TIMIT speech dataset demonstrate that the proposed method significantly outperforms the state-of-the-art method in terms of accuracy and robustness against various MP3 compression.
Domain Adaptive Image-to-Image Translation

Unpaired image-to-image translation (I2I) has achieved great success in various applications. However, its generalization capacity is still an open question. In this paper, we show that existing I2I models do not generalize well for samples outside the training domain. The cause is twofold. First, an I2I model may not work well when testing samples are beyond its valid input domain. Second, results could be unreliable if the expected output is far from what the model is trained. To deal with these issues, we propose the Domain Adaptive Image-To-Image translation (DAI2I) framework that adapts an I2I model for out-of-domain samples. Our framework introduces two sub-modules -- one maps testing samples to the valid input domain of the I2I model, and the other transforms the output of I2I model to expected results. Extensive experiments manifest that our framework improves the capacity of existing I2I models, allowing them to handle samples that are distinctively different from their primary targets.
Peek-a-Boo: Occlusion Reasoning in Indoor Scenes With Plane Representations

We address the challenging task of occlusion-aware indoor 3D scene understanding. We represent scenes by a set of planes, where each one is defined by its normal, offset and two masks outlining (i) the extent of the visible part and (ii) the full region that consists of both visible and occluded parts of the plane. We infer these planes from a single input image with a novel neural network architecture. It consists of a two-branch category-specific module that aims to predict layout and objects of the scene separately so that different types of planes can be handled better. We also introduce a novel loss function based on plane warping that can leverage multiple views at training time for improved occlusion-aware reasoning. In order to train and evaluate our occlusion-reasoning model, we use the ScanNet dataset and propose (i) a strategy to automatically extract ground truth for both visible and hidden regions and (ii) a new evaluation metric that specifically focuses on the prediction in hidden regions. We empirically demonstrate that our proposed approach can achieve higher accuracy for occlusion reasoning compared to competitive baselines on the ScanNet dataset, e.g. 42.65% relative improvement on hidden regions.
Primal-Dual Stochastic Gradient Method for Convex Programs with Many Functional Constraints

The stochastic gradient method (SGM) has been popularly applied to solve optimization problems with an objective that is stochastic or an average of many functions. Most existing works on SGMs assume that the underlying problem is unconstrained or has an easy-to-project constraint set. In this paper, we consider problems that have a stochastic objective and also many functional constraints. For such problems, it could be extremely expensive to project a point to the feasible set, or even compute subgradient and/or function value of all constraint functions. To find solutions to these problems, we propose a novel (adaptive) SGM based on the classical augmented Lagrangian function. Within every iteration, it inquires a stochastic subgradient of the objective, and a subgradient and the function value of one randomly sampled constraint function. Hence, the per-iteration complexity is low. We establish its convergence rate for convex problems and also problems with strongly convex objective. It can achieve the optimal $O(1/\sqrt{k})$ convergence rate for the convex case and nearly optimal $O\big((\log k)/k\big)$ rate for the strongly convex case. Numerical experiments on a sample approximation problem of the robust portfolio selection and quadratically constrained quadratic programming are conducted to demonstrate its efficiency.
Realizing Quantum Algorithms on Real Quantum Computing Devices

Quantum computing is currently moving from an academic idea to a practical reality. Quantum computing in the cloud is already available and allows users from all over the world to develop and execute real quantum algorithms. However, companies which are heavily investing in this new technology such as Google, IBM, Rigetti, Intel, IonQ, and Xanadu follow diverse technological approaches. This led to a situation where we have substantially different quantum computing devices available thus far. They mostly differ in the number and kind of qubits and the connectivity between them. Because of that, various methods for realizing the intended quantum functionality on a given quantum computing device are available. This paper provides an introduction and overview into this domain and describes corresponding methods, also referred to as compilers, mappers, synthesizers, transpilers, or routers.
Current state of research in application of disruptive technologies in engineering education

Disruptive innovation technologies have made their way in the field of academia during the past few years. A plethora of literature exists investigating the applications of various disruptive technologies in the field of engineering education. The directions for future research however are still obscured by the copious amount of literature having split opinions and conflicting results which necessitates a review of current state of the research. This paper clarifies the underlying concept of the theory. The authors then critique and summarize the research on present state of implementation mainly focusing on the field of engineering education. The future scope for the research is also discussed keeping in the mind the upcoming new technologies such as mobilecomputing, wearable technologiesand internet of things combined with machine learning.