Reviewerly/exHarmony huggingface dataset:
Verifiable Registration-Based Encryption

In recent work, Garg, Hajiabadi, Mahmoody, and Rahimi [18] introduced a new encryption framework, which they referred to as Registration-Based Encryption (RBE). The central motivation behind RBE was to provide a novel methodology for solving the well-known key-escrow problem in Identity-Based Encryption (IBE) systems [33]. Informally, in an RBE system, there is no private-key generator unlike IBE systems, but instead, it is replaced with a public key accumulator. Every user in an RBE system samples its own public-secret key pair and sends the public key to the accumulator for registration. The key accumulator has no secret state and is only responsible for compressing all the registered user identity-key pairs into a short public commitment. Here the encryptor only requires the compressed parameters along with the target identity, whereas a decryptor requires supplementary key material along with the secret key associated with the registered public key. The initial construction in [18] based on standard assumptions only provided weak efficiency properties. In a follow-up work by Garg, Hajiabadi, Mahmoody, Rahimi, and Sekar [19], they gave an efficient RBE construction from standard assumptions. However, both these works considered the key accumulator to be honest which might be too strong an assumption in real-world scenarios. In this work, we initiate a formal study of RBE systems with malicious key accumulators. To that end, we introduce a strengthening of the RBE framework which we call Verifiable RBE (VRBE). A VRBE system additionally gives the users an extra capability to obtain short proofs from the key accumulator proving correct (and unique) registration for every registered user as well as proving non-registration for any yet unregistered identity. We construct VRBE systems that provide succinct proofs of registration and non-registration from standard assumptions (such as CDH, Factoring, LWE). Our proof systems also naturally allow a much more efficient audit process which can be performed by any non-participating third party as well. A by-product of our approach is that we provide a more efficient RBE construction than that provided in the prior work of Garg et al. [19]. And lastly, we initiate a study on the extension of VRBE to a wider range of access and trust structures.
Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources

Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.
SCHOOL PRINCIPALS TRANSFORMATIONAL LEADERSHIP STYLES AND THEIR EFFECTS ON TEACHERS SELF-EFFICACY

15Oct 2019 SCHOOL PRINCIPALS TRANSFORMATIONAL LEADERSHIP STYLES AND THEIR EFFECTS ON TEACHERS SELF-EFFICACY. Christopher DC. Francisco. City of Malolos Integrated School - Sto. Rosario, Sto. Rosario, City of Malolos, Bulacan.
Towards Effective Device-Aware Federated Learning

With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users’ personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means to leave data and computational resources distributed over a large number of nodes (clients) where a central coordinating server aggregates only locally computed updates without knowing the original data. In this work, we extend the FL framework by pushing forward the state the art in the field on several dimensions: (i) unlike the original FedAvg approach relying solely on single criteria (i.e., local dataset size), a suite of domain- and client-specific criteria constitute the basis to compute each local client’s contribution, (ii) the multi-criteria contribution of each device is computed in a prioritized fashion by leveraging a priority-aware aggregation operator used in the field of information retrieval, and (iii) a mechanism is proposed for online-adjustment of the aggregation operator parameters via a local search strategy with backtracking. Extensive experiments on a publicly available dataset indicate the merits of the proposed approach compared to standard FedAvg baseline.
Deep-learning approaches for Gleason grading of prostate biopsies

Gleason grades are assigned by pathologists based on prostate cancer morphology to describe the loss of tissue structure and order1 and are strongly correlated with disease aggressiveness and patient outcome.2 Gleason scoring categorises tumour tissue into patterns from 1 (low risk) to 5 (high risk). Although Gleason grade has long been recognised as being strongly associated with risk of prostate cancer recurrence and metastasis, substantial inter-observer disagreement exists, especially with respect to the intermediate grades.
A New GAN-Based Anomaly Detection (GBAD) Approach for Multi-Threat Object Classification on Large-Scale X-Ray Security Images

Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.
The business benefits of cyber security for SMEs

Cyber security is often discussed in terms of data breaches, regulatory fines and business disruption. The advantages are seldom highlighted. For example, effective cyber security makes it possible for companies to innovate and this drives revenue, profit and growth. Defending against cybercrime can deliver genuine benefits for small and medium-size enterprises (SMEs) and result in more valuable organisations. Cyber security is often discussed in terms of data breaches, regulatory fines and business disruption. However, the advantages are seldom highlighted. Effective cyber security makes it possible for companies to innovate and this drives revenue, profit and growth. Defending against cybercrime can deliver genuine benefits for small and medium-size enterprises. And as organisations of all sizes move towards driving efficiency through digitising processes, it's important for business leaders to redefine how they think about security, argues Guy Lloyd of CySure.
Ensemble models for solar power forecasting—a weather classification approach

Solar power integration has shown a significant growth in many power systems during the last decade. The intermittent nature of solar irradiance tends to vary the amount of solar power in the system and an accurate solar power forecasting method can be used to tackle this in power system planning and operation. In this paper, authors have proposed a generalized ensemble model integrating deep learning techniques to generate accurate solar power forecasts for 21 solar photovoltaic facilities located in Germany. Most important weather parameters for solar power generation are selected through a feature selection process. In addition, a weather classification approach is used to cluster the dataset and for each cluster, a separate ensemble algorithm is assigned. Finally, considering the prediction errors in each cluster, a novel ensemble model is developed. The proposed models are evaluated using root mean square error and results are compared with single machine learning techniques and available forecasting models in the literature. Compared to deep belief network, support vector regression and random forest regression models, the proposed ensemble model with cloud classification reduces RMSE error by 10.49%, 7.78%, and 7.95% respectively. Results show that the weather classification approach reduces the forecasting error by a considerable margin and the proposed ensemble model provides a better forecasting accuracy than single machine learning methods.
From automats to algorithms: the automation of services using artificial intelligence

Purpose The paper aims to fill this gap by positing a framework that considers the service automation decision as a matter of knowledge management: a choice between human resident and codified knowledge assets. Design/methodology/approach The paper is a conceptual paper, grounded in the knowledge-based view. Findings The paper uses the information processing theory, which argues that the level of uncertainty in a process should dictate the type of knowledge deployed, as the contingency for the automation choice, and customer interaction uncertainty as the driver of that contingency. From these ideas, propositions are generated relating customer interaction uncertainty and service automation. Further implications for artificial intelligence (AI) are also explored. Originality/value The framework illuminates and informs the strategic choices regarding service automation, including the use of AI in professional services, a timely and highly important topic. It offers a valuable model for practitioners and contributes to the academic literature by pointing the way for future directions for scholarly research.
Multimodal and multiview distillation for real-time player detection on a football field

Monitoring the occupancy of public sports facilities is essential to assess their use and to motivate their construction in new places. In the case of a football field, the area to cover is large, thus several regular cameras should be used, which makes the setup expensive and complex. As an alternative, we developed a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera. In this work, we train a network in a knowledge distillation approach in which the student and the teacher have different modalities and a different view of the same scene. In particular, we design a custom data augmentation combined with a motion detection algorithm to handle the training in the region of the fisheye camera not covered by the thermal one. We show that our solution is effective in detecting players on the whole field filmed by the fisheye camera. We evaluate it quantitatively and qualitatively in the case of an online distillation, where the student detects players in real time while being continuously adapted to the latest video conditions.
Hyperspectral image denoising by total variation-regularized bilinear factorization

Hyperspectral image (HSI) denoising is a prevalent research topic in the remote sensing area. In general, HSIs are inevitably impaired by different types of noise during the data acquisition. To fully characterize the underlying structures of clean HSI and remove mixed noises, we introduce a novel HSI denoising method named total variation-regularized bilinear factorization (BFTV) model. Specifically, we first utilize the bilinear factorization term to explore the globally low-rank structure of the clean HSI and suppress a certain degree of Gaussian noise, so as to make BFTV free to the singular value decomposition. Then the l1-norm is applied to detect and separate the mixed sparse noise including impulse noise, deadlines, and stripes. Besides, the TV regularization is introduced to describe the locally piecewise smoothness property of the clean HSI both in spatial and spectral domains. To solve this optimization problem, we devise an effective algorithm based on the augmented Lagrange multiplier method. Numerical experiments on five different kinds of mixed noise scenarios and one real world data have tested and demonstrated the superior denoising power of the proposed BFTV model compared with three state-of-the-art low-rank-based approaches.
Measuring continuous-variable quantum entanglement with parametric-amplifier-assisted homodyne detection

The traditional method for measuring Einstein-Podolsky-Rosen-type continuous-variable quantum entanglement relies on balanced homodyne detections, which are sensitive to vacuum quantum noise coupled in through losses due to various factors such as detector quantum efficiency and mode mismatching between the detected field and the local oscillator. In this paper, we propose and analyze a measurement method, which is realized by assisting the balanced homodyne detections with a high-gain phase-sensitive parametric amplifier. The employment of the phase-sensitive amplifier helps us to tackle the vacuum quantum noise originating from detection losses. Moreover, because the high-gain phase-sensitive amplifier can couple two fields of different types, the proposed scheme can be used to reveal quantum entanglement between two fields of different types by using only one balanced homodyne detection. Furthermore, detailed analysis shows that in the multimode case, the proposed scheme is also advantageous over the traditional method. Such a measurement method should find wide applications in quantum information and quantum metrology involving measurement of continuous variables.
Integrating the Digital Paradigm in Higher Education: ICT Training and Skills of University Students in a European Context

The main objective of this study is to analyse the basic digital skills of university students.A sample of 490 students of two degrees from a Spanish university was used from the 2013/14 to 2016/17 academic year.The methodology is descriptive of a quantitative nature.The data were collected by means of the questionnaire called "Basic digital competences 2.0 of the university students" COBADI® (Registered trademark: 2970648).The statistical analysis provided information on how students use the Internet and digital technologies.Among the results obtained it could be determined that a high percentage of the respondents were connected to the Internet more than 9 hours a week.The most frequently used means of connection is the computer, at home, and the smartphone.A significant finding is that, in the case of men, there is a decrease in time spent on academic tasks in favour of time spent on videogames.
A Novel Statistical Feature Analysis-Based Global and Local Method for Face Recognition

Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images. As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the image-based face recognition system with a higher accuracy rate. To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps. To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images. The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern. Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods. The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.
Large Scale Multi-Actor Generative Dialog Modeling

Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and engaging conversations with a user; however, they typically exhibit either inconsistent personality across conversations or the average personality of all users. This paper addresses these issues by controlling an agent’s persona upon generation via conditioning on prior conversations of a target actor. In doing so, we are able to utilize more abstract patterns within a person’s speech and better emulate them in generated responses. This work introduces the Generative Conversation Control model, an augmented and fine-tuned GPT-2 language model that conditions on past reference conversations to probabilistically model multi-turn conversations in the actor’s persona. We introduce an accompanying data collection procedure to obtain 10.3M conversations from 6 months worth of Reddit comments. We demonstrate that scaling model sizes from 117M to 8.3B parameters yields an improvement from 23.14 to 13.14 perplexity on 1.7M held out Reddit conversations. Increasing model scale yielded similar improvements in human evaluations that measure preference of model samples to the held out target distribution in terms of realism (31% increased to 37% preference), style matching (37% to 42%), grammar and content quality (29% to 42%), and conversation coherency (32% to 40%). We find that conditionally modeling past conversations improves perplexity by 0.47 in automatic evaluations. Through human trials we identify positive trends between conditional modeling and style matching and outline steps to further improve persona control.
DYNOTEARS: Structure Learning from Time-Series Data

We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slice) relationships between variables in a time-series. Our approach is score-based, and revolves around minimizing a penalized loss subject to an acyclicity constraint. To solve this problem, we leverage a recent algebraic result characterizing the acyclicity constraint as a smooth equality constraint. The resulting algorithm, which we call DYNOTEARS, outperforms other methods on simulated data, especially in high-dimensions as the number of variables increases. We also apply this algorithm on real datasets from two different domains, finance and molecular biology, and analyze the resulting output. Compared to state-of-the-art methods for learning dynamic Bayesian networks, our method is both scalable and accurate on real data. The simple formulation and competitive performance of our method make it suitable for a variety of problems where one seeks to learn connections between variables across time.
Compressing Proofs of k-Out-Of-n Partial Knowledge

In a proof of partial knowledge, introduced by Cramer, Damgård and Schoenmakers (CRYPTO 1994), a prover knowing witnesses for some k-subset of n given public statements can convince the verifier of this claim without revealing which k-subset. Their solution combines $$\varSigma $$ -protocol theory and linear secret sharing, and achieves linear communication complexity for general k, n. Especially the “one-out-of-n” case $$k=1$$ has seen myriad applications during the last decades, e.g., in electronic voting, ring signatures, and confidential transaction systems. In this paper we focus on the discrete logarithm (DL) setting, where the prover claims knowledge of DLs of k-out-of-n given elements. Groth and Kohlweiss (EUROCRYPT 2015) have shown how to solve the special case $$k=1$$ with logarithmic (in n) communication, instead of linear as prior work. However, their method takes explicit advantage of $$k=1$$ and does not generalize to $$k>1$$ . Alternatively, an indirect approach for solving the considered problem is by translating the k-out-of-n relation into a circuit and then applying communication-efficient circuit ZK. For $$k = 1$$ this approach has been highly optimized, e.g., in ZCash. Our main contribution is a new, simple honest-verifier zero-knowledge proof protocol for proving knowledge of k out of n DLs with logarithmic communication and for general k and n, without requiring any generic circuit ZK machinery. Our solution puts forward a novel extension of the compressed $$\varSigma $$ -protocol theory (CRYPTO 2020), which we then utilize to compress a new $$\varSigma $$ -protocol for proving knowledge of k-out-of-n DL’s down to logarithmic size. The latter $$\varSigma $$ -protocol is inspired by the CRYPTO 1994 approach, but a careful re-design of the original protocol is necessary for the compression technique to apply. Interestingly, even for $$k=1$$ and general n our approach improves prior direct approaches as it reduces prover complexity without increasing the communication complexity. Besides the conceptual simplicity, we also identify regimes of practical relevance where our approach achieves asymptotic and concrete improvements, e.g., in proof size and prover complexity, over the generic approach based on circuit-ZK. Finally, we show various extensions and generalizations of our core result. For instance, we extend our protocol to proofs of partial knowledge of Pedersen (vector) commitment openings, and/or to include a proof that the witness satisfies some additional constraint, and we show how to extend our results to non-threshold access structures.
Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the illusion of always-available resources (i.e., fast function invocations without cold starts) at the lowest possible resource cost. Doing so requires the provider to deeply understand the characteristics of the FaaS workload. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we ﬁrst characterize the entire production FaaS workload of Azure Functions. We show for example that most functions are invoked very infrequently, but there is an 8-order-of-magnitude range of invocation frequencies. Using observations from our characterization, we then propose a practical resource management policy that signiﬁcantly reduces the number of function cold starts, while spending fewer resources than state-of-the-practice policies.