Reviewerly/exHarmony huggingface dataset:
Character Segmentation-Based Coarse-Fine Approach for Automobile Dashboard Detection

Computer vision based detection approaches are widely employed to detect or calibrate different types of meters nowadays. However, traditional detection algorithms suffer drawbacks in accuracy and adaptability upon detecting various types of automobile dashboards. Plenty of parameters of these algorithms need to be tuned to suit certain types of dashboards. Besides, theses algorithms cannot automatically read the speed value, which requires manual setting operations. In this paper, a novel approach is presented to adaptively detect different types of automobile dashboards. The contour analysis based method is first implemented to extract the connected component of the pointer. A robust character segmentation classifier, which is designed by cascading histogram of oriented gradients (HOG)/support vector machine (SVM) binary classifier, character filter as well as HOG/multiclass SVM digit classifier, is then proposed to recognize digit characters on the dashboard. Simultaneously, tick marks are then extracted based on recognition results. Finally, Newton interpolation linear relationship is established to diagnose the potential responding errors of the pointer. The experimental results show that the pointer extraction method is robust to interferences caused by connected components of digits and also that the established character segmentation classifier has a more accurate detection result. Furthermore, compared with similar algorithms, it has a significant advantage in detecting a vast majority of different dashboards without manual tuning of the parameters.
EVM: From Offline Detection to Online Reinforcement for Ethereum Virtual Machine

Attacks on transactions of Ethereum could be dangerous because they could lead to a big loss of money. There are many tools detecting vulnerabilities in smart contracts trying to avoid potential attacks. However, we found that there are still many missed vulnerabilities in contracts. Motivated by this, we propose a methodology to reinforce EVM to stop dangerous transactions in real time even when the smart contract contains vulnerabilities. Basically, the methodology consists of three steps: monitoring strategy definition, opcode-structure maintenance and EVM instrumentation. Monitoring strategy definition refers to the specific rule to test whether there is a dangerous operation during transaction execution. Opcode-structure maintenance is to maintain a structure to store the rule related opcodes and analyze it before an operation execution. EVM instrumentation inserts the monitoring strategy, interrupting mechanism and the opcode-structure operations in EVM source code. For evaluation, we implement EVM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> on js-evm, a widely-used EVM platform written in javascript. We collect 10 contracts online with known bugs and use each contract to execute a dangerous transaction, all of them have been interrupted by our reinforced EVM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , while the original EVM permits all attack transactions. For the time overhead, the reinforced EVM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> is slower than the original one by 20-30%, which is tolerable for the financial critical applications.
Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction

The process of frequent itemset mining (FIM) within large-scale databases plays a significant part in many knowledge discovery tasks, where, however, potential privacy breaches are possible. Privacy preserving frequent itemset mining (PPFIM) has thus drawn increasing attention recently, where the ultimate goal is to hide sensitive frequent itemsets (SFIs) so as to leave no confidential knowledge uncovered in the resulting database. Nevertheless, the vast majority of the proposed methods for PPFIM were merely based on database perturbation, which may result in a significant loss of data utility in order to conceal all SFIs. To alleviate this issue, this paper proposes a database reconstruction-based algorithm for PPFIM (DR-PPFIM) that can not only achieve a high degree of privacy but also afford a reasonable data utility. In DR-PPFIM, all SFIs with related frequent itemsets are first identified for removing in the pre-sanitize process by implementing a devised sanitize method. With the remained frequent itemsets, a novel database reconstruction scheme is proposed to reconstruct an appropriate database, where the concepts of inverse frequent itemset mining (IFIM) and database extension are efficiently integrated. In this way, all SFIs are able to be hidden under the same mining threshold while maximizing the data utility of the synthetic database as much as possible. Moreover, we also develop a further hiding strategy in DR-PPFIM to further decrease the significance of SFIs with the purpose of reducing the risk of disclosing confidential knowledge. Extensive comparative experiments are conducted on real databases to demonstrate the superiority of DR-PPFIM in terms of maximizing the utility of data and resisting potential threats.
A survey on human detection surveillance systems for Raspberry Pi

Building reliable surveillance systems is critical for security and safety. A core component of any surveillance system is the human detection model. With the recent advances in the hardware and embedded devices, it becomes possible to make a real-time human detection system with low cost. This paper surveys different systems and techniques that have been deployed on embedded devices such as Raspberry Pi. The characteristics of datasets, feature extraction techniques, and machine learning models are covered. A unified dataset is utilized to compare different systems with respect to accuracy and performance time. New enhancements are suggested, and future research directions are highlighted.
Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks

Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd.Most of the existing approaches use regression on density maps for the crowd count from a single image.However, these methods cannot localize individual pedestrian and therefore cannot estimate the actual distribution of pedestrians in the environment.On the other hand, detection-based methods detect and localize pedestrians in the scene, but the performance of these methods degrades when applied in high-density situations.To overcome the limitations of pedestrian detectors, we proposed a motion-guided filter (MGF) that exploits spatial and temporal information between consecutive frames of the video to recover missed detections.Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos.We employ various state-of-the-art network architectures, namely, Visual Geometry Group (VGG16), Zeiler and Fergus (ZF), and VGGM in the framework of a region-based DCNN for detecting pedestrians.After pedestrian detection, the proposed motion guided filter is employed.We evaluate the performance of our approach on three publicly available datasets.The experimental results demonstrate the effectiveness of our approach, which significantly improves the performance of the state-of-the-art detectors.
The <i>p</i>-value Function and Statistical Inference

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Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN Framework

Recently, deep learning has become a preferred choice for performing tasks in diverse application domains such as computer vision, natural language processing, sensor data analytics for healthcare, and collaborative filtering for personalized item recommendation. In addition, the Generative Adversarial Networks (GAN) has become one of the most popular frameworks for training machine learning models. Motivated by the huge success of GAN and deep learning on a wide range of fields, this paper explores an effective way to exploit both techniques into the collaborative filtering task for the accurate recommendation. We have noticed that the IRGAN and GraphGAN are pioneering methods that successfully apply GAN to recommender systems. However, we point out an issue regarding the employment of standard matrix factorization (MF) as their basic model, which is linear and unable to capture the non-linear, subtle latent factors underlying user-item interactions. Our proposed recommendation framework, named Collaborative Adversarial Autoencoders (CAAE), significantly extends the conventional IRGAN and GraphGAN as summarized below: 1) we use Autoencoder, which is one of the most successful deep neural networks, as our generator, instead of using the MF model; 2) we employ Bayesian personalized ranking (BPR) as our discriminative model; and 3) we incorporate another generator model into our framework that focuses on generating negative items, which are items that a given user may not be interested in. We empirically test our framework using three real-life datasets along with four evaluation metrics. Owing to those extensions, our proposed framework not only produces considerably higher recommendation accuracy than the conventional GAN-based recommenders (i.e., IRGAN and GraphGAN), but also outperforms the other state-of-the-art top-N recommenders (i.e., BPR, PureSVD, and FISM).
Image classification base on PCA of multi-view deep representation

In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classification algorithms are mostly based on RGB images or grayscale images, and fail to make good use of the depth information about objects or scenes. The depth information in the images has a strong complementary effect, which can enhance the classification accuracy significantly. In this paper, we propose an image classification technology using principal component analysis based on multi-view depth characters. In detail, firstly, the depth image of the original image is estimated; secondly, depth characters are extracted from the RGB views and the depth view separately, and then the reducing dimension operation through the PCA is implemented. Eventually, the SVM is applied to image classification. The experimental results show that the method has good performance.
Understanding online anonymity

Whenever we navigate the Web, we leave a trace through our IP address, which can in turn be used to establish our identity – for instance, by cross-checking it with a user’s Internet subscription. By using software such as VPN and Tor, however, it might be possible to avoid leaving such traces. A lively debate among policymakers, security professionals, hacker communities, and human rights associations has recently ensued regarding the question if such anonymity is acceptable and in which form. This article introduces the Crosscurrent special section dedicated to this topic by providing a brief overview of this debate and by pointing to the necessity of considering online anonymity from multiple, interrelated perspectives. By taking into account both technical and social dimensions, we argue that online anonymity should not be conceptualized in absolute terms but as an inherently fluid and transitional condition that characterizes any kind of social interaction online.
Fast Distributed Deep Learning over RDMA

Deep learning emerges as an important new resource-intensive workload and has been successfully applied in computer vision, speech, natural language processing, and so on. Distributed deep learning is becoming a necessity to cope with growing data and model sizes. Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a dataflow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.
An educational IoT lab kit and tools for energy awareness in European schools

The use of maker community tools and IoT technologies inside classrooms is spreading to an ever-increasing number of education and science fields. GAIA is a European research project focused on achieving behavior change for sustainability and energy awareness in schools. In this work, we report on how a large IoT deployment in a number of educational buildings and real-world data from this infrastructure, are utilized to support a "maker" lab kit activity inside the classroom. We also provide some insights to the integration of these activities in the school curriculum, along with a discussion on feedback produced through a series of workshop activities in a number of schools in Greece. Moreover, we discuss the application of the lab kit framework towards implementing an interactive installation. We also report on how the lab kit is paired with a serious game and an augmented reality app for smartphones and tablets, supporting the in-class activities. Our initial evaluation results show a very positive first reaction by the school community.
Interference of Temporally Distinguishable Photons Using Frequency-Resolved Detection

We demonstrate quantum interference of three photons that are distinguishable in time, by resolving them in the conjugate parameter, frequency. We show that the multiphoton interference pattern in our setup can be manipulated by tuning the relative delays between the photons, without the need for reconfiguring the optical network. Furthermore, we observe that the symmetries of our optical network and the spectral amplitude of the input photons are manifested in the interference pattern. Moreover, we demonstrate time-reversed HOM-like interference in the spectral correlations using time-bin entangled photon pairs. By adding a time-varying dispersion using a phase modulator, our setup can be used to realize dynamically reconfigurable and scalable boson sampling in the time domain as well as frequency-resolved multiboson correlation sampling.
On the computation of distribution-free performance bounds: Application to small sample sizes in neuroimaging

In this paper we derive practical and novel upper bounds for the resubstitution error estimate by assessing the number of linear decision functions within the problem of pattern recognition in neuroimaging. Linear classifiers and regressors have been considered in many fields, where the number of predictors far exceeds the number of training samples available, to overcome the limitations of high complexity models in terms of computation, interpretability and overfitting. Typically in neuroimaging this is the rule rather than the exception, since the dimensionality of each observation (millions of voxels) in relation to the number of available samples (hundred of scans) implies a high risk of overfitting. Based on classical combinatorial geometry, we estimate the number of hyperplanes or linear decision rules and the corresponding distribution-independent performance bounds, comparing it to those obtained by the use of the VC-dimension concept. Experiments on synthetic and neuroimaging data demonstrate the performance of resubstitution error estimators, which are often overlooked in heterogeneous scenarios where their performance is similar to that obtained by cross-validation methods.
A Risk Mitigation Framework for Information Technology Projects: A Cultural Contingency Perspective

As new information technology (IT) platforms continue to emerge, the technical project risk associated with developing IT projects for these platforms is particularly challenging for organizations. We develop a nomological network of people, process, and technology to gain insight into how the effect of technical project risk can be mitigated at the IT project team level. Drawing on cultural contingency theory, the IT project risk framework, and the IT project management literature, we elaborate on the IT project team composition and team processes necessary to mitigate technical project risk. We tested the nomological network by conducting a field study of 325 IT project teams over a 3-year period at a large corporation in China. We found that project risk mitigation processes mediated the effect of IT project teams’ cultural composition on IT project performance, and the effect of these processes on IT project performance was stronger under high levels of technical project risk compared to low levels of such risk. By incorporating the cultural contingency theory into developing a nomological network of technical risk mitigation processes, this work not only contributes to the IT project management literature, but also provides suggestions for practitioners on how to better leverage people, process, and technology in mitigating IT project risks.
Telesuit: An Immersive User-Centric Telepresence Control Suit

Telepresence takes place when a user is afforded with the experience of being in a remote environment or virtual world, through the use of immersive technologies. A humanoid robot and a control apparatus that correlates the operator's movements whilst providing sufficient sensory feedback, encompass such immersive technologies. This paper considers the control mechanisms that afford telepresence, the requirements for continuous or extended telepresence control, and the health implications of engaging in complex time-constrained tasks. We present Telesuit - a full-body telepresence control system for operating a humanoid telepresence robot. The suit is part of a broader system that considers the constraints of controlling a dexterous bimanual robotic torso, and the need for modular hardware and software that allows for high-fidelity immersiveness. It incorporates a health-monitoring system that collects information such as respiratory effort, galvanic skin response, and heart rate. This information is consequently leveraged by the platform to adjust the telepresence experience and apply control modalities for autonomy. Furthermore, the design of the Telesuit garment considers both functionality and aesthetics.
Blockchain-Based SLA Management in the Context of IoT

In pursuit of effective service level agreement (SLA) monitoring and enforcement in the context of Internet of Things (IoT) applications, this article regards SLA management as a distrusted process that should not be handled by a single authority. Here, we aim to justify our view on the matter and propose a conceptual blockchain-based framework to cope with some limitations associated with traditional SLA management approaches.
A manifold learning approach to dimensionality reduction for modeling data

Manifold learning has gained in recent years a great attention in facing the problem of dimensionality reduction of high-dimensional data. This technique is based on the assumption that data are embedded in a nonlinear manifold of lower dimension. In this context the dimension of the embedding is a key parameter, therefore is of paramount importance for dimensionality reduction to discover the appropriate dimensionality of the reduced feature space, that is the intrinsic dimension (ID) of data. The purpose of this paper is to derive a manifold learning approach to dimensionality reduction for modeling data coming from either causal or noncausal signals. The approach is based on some theoretical results that aim first at giving a practical method for the estimation of the intrinsic dimension and then at deriving a local parametrization of data. Besides, an explicit nonlinear mapping relationship from data to the reduced space can be obtained as the regression of a nonlinear function. Several experiments on both synthetic and real data for the two classes of causal and noncausal signals have been conducted to validate the proposed approach.
Sentiment-Aware Word Embedding for Emotion Classification

Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks.
Secret-Sharing Schemes for General and Uniform Access Structures

A secret-sharing scheme allows some authorized sets of parties to reconstruct a secret; the collection of authorized sets is called the access structure. For over 30 years, it was known that any (monotone) collection of authorized sets can be realized by a secret-sharing scheme whose shares are of size $$2^{n-o(n)}$$ and until recently no better scheme was known. In a recent breakthrough, Liu and Vaikuntanathan (STOC 2018) have reduced the share size to $$O(2^{0.994n})$$ . Our first contribution is improving the exponent of secret sharing down to 0.892. For the special case of linear secret-sharing schemes, we get an exponent of 0.942 (compared to 0.999 of Liu and Vaikuntanathan). Motivated by the construction of Liu and Vaikuntanathan, we study secret-sharing schemes for uniform access structures. An access structure is k-uniform if all sets of size larger than k are authorized, all sets of size smaller than k are unauthorized, and each set of size k can be either authorized or unauthorized. The construction of Liu and Vaikuntanathan starts from protocols for conditional disclosure of secrets, constructs secret-sharing schemes for uniform access structures from them, and combines these schemes in order to obtain secret-sharing schemes for general access structures. Our second contribution in this paper is constructions of secret-sharing schemes for uniform access structures. We achieve the following results: Our third contribution is a construction of ad-hoc PSM protocols, i.e., PSM protocols in which only a subset of the parties will compute a function on their inputs. This result is based on ideas we used in the construction of secret-sharing schemes for k-uniform access structures for a binary secret.