Reviewerly/exHarmony huggingface dataset:
AC^0[p] Lower Bounds Against MCSP via the Coin Problem

Minimum Circuit Size Problem (MCSP) asks to decide if a given truth table of an n-variate boolean function has circuit complexity less than a given parameter s. We prove that MCSP is hard for constant-depth circuits with mod p gates, for any prime p >= 2 (the circuit class AC^0[p]). Namely, we show that MCSP requires d-depth AC^0[p] circuits of size at least exp(N^{0.49/d}), where N=2^n is the size of an input truth table of an n-variate boolean function. Our circuit lower bound proof shows that MCSP can solve the coin problem: distinguish uniformly random N-bit strings from those generated using independent samples from a biased random coin which is 1 with probability 1/2+N^{-0.49}, and 0 otherwise. Solving the coin problem with such parameters is known to require exponentially large AC^0[p] circuits. Moreover, this also implies that MAJORITY is computable by a non-uniform AC^0 circuit of polynomial size that also has MCSP-oracle gates. The latter has a few other consequences for the complexity of MCSP, e.g., we get that any boolean function in NC^1 (i.e., computable by a polynomial-size formula) can also be computed by a non-uniform polynomial-size AC^0 circuit with MCSP-oracle gates.
Female Facial Beauty Analysis Using Transfer Learning and Stacking Ensemble Model

Automatic analysis of facial beauty has become an emerging research topic in recent years and has fascinated many researchers. One of the key challenges of facial attractiveness prediction is to obtain accurate and discriminative face representation. This study provides a new framework to analyze the attractiveness of female faces using transfer learning methodology as well as stacking ensemble model. Specifically, a pre-trained Convolutional Neural Network (CNN) originally trained on relatively similar datasets for face recognition task, namely Ms-Celeb-1M and VGGFace2, is utilized to acquire high-level and robust features of female face images. This is followed by leveraging a stacking ensemble model which combines the predictions of several base models to predict the attractiveness of a face. Extensive experiments conducted on SCUT-FBP and SCUT-FBP 5500 benchmark datasets, confirm the strong robustness of the proposed approach. Interestingly, prediction correlations of 0.89 and 0.91 are achieved by our new method for SCUT-FBP and SCUT-FBP5500 datasets, respectively. This would indicate significant advantages over the other state-of-the-art work. Moreover, our successful results would certainly support the efficacy of transfer learning when applying deep learning techniques to compute facial attractiveness.
Virtual Hand Illusion: The Alien Finger Motion Experiment

In Virtual Reality, the need to understand how subjects perceive their representation is gaining attention and importance. We present a contribution to a better understanding of the sense of embodiment by assessing two of its main components, body ownership and agency, through an experiment involving alien motion. The key aspect of the experimental protocol is to integrate a condition with some personalized alien finger movement while the subject is asked to remain still. Body ownership appears to be significantly reduced, but not agency. We also propose a metric to assess quantitatively that the view of the alien movement induces more finger posture variation compared to the reference context in the still condition.
Measuring effectiveness of control of information security management system based on SNI ISO/IEC 27004: 2013 standard

Abstract One of the keys to the successful implementation of information security management in an organization is the selection and implementation of an information security management system control that is good and in accordance with the needs of the organization, the information security management system control can be adopted based on ISO/IEC 27001: 2013 standard document. To ensure the success of information security controls, it is necessary to measure the effectiveness of each control applied. SNI ISO/IEC 27004: 2013 is a standard that provides guidance on the development and use of measures and measurements to assess the effectiveness of controls and control groups in the information security management system as stated in the ISO/IEC 27001 standard, but to do the measurement process, required objects and measurement attributes and metrics, which are not explained in detail in the ISO ISO/IEC 27004: 2013 standard. This study aims to assist in measuring the effectiveness of information security management control by generating the flow of steps in determining the object and measurement parameters and the metrics used based on the provisions contained in the ISO ISO/IEC 27004: 2013 standard.
MProve: A Proof of Reserves Protocol for Monero Exchanges

Theft from cryptocurrency exchanges due to cyberattacks or internal fraud is a major problem. Exchanges can partially alleviate customer concerns by providing periodic proofs of solvency. We describe MProve, a proof of reserves protocol for Monero exchanges which can be combined with a known proof of liabilities protocol to provide a proof of solvency. It is the first protocol for Monero which provides address privacy by allowing an exchange to hide its own addresses within a larger anonymity set. MProve also provides a simple proof of non-collusion between exchanges.
Two-channel depth encoding for 3D range geometry compression

This paper presents a novel method for accurately encoding 3D range geometry within only two channels of a 2D RGB image using a two-frequency phase-shifting approach. Once encoded within a 2D image, 3D geometry can be further compressed with conventional lossless or lossy image compression methods. The nature of the proposed two-channel encoding is relatively smooth; thus, large compression ratios with high reconstruction accuracies can be achieved and are experimentally demonstrated. For example, a compression ratio of 2883:1 was achieved, compared with the STL format, with a reconstruction RMS error of 0.45 mm (99.8% accuracy) when JPEG 85 was used with the proposed method. This paper also demonstrates how a 24-bit color texture map can be encoded alongside 3D geometry within a single 2D image.
Combining Tablets with Smartphones for Data Analytics

While ubiquitous data analytics is a promising approach, analyzing spreadsheets data on tablets is a tedious task due to the limited display size and touch vocabulary. In this paper, we present the design and evaluation of novel interaction techniques relying on the combination of a tablet, holding the data, and a smartphone, used as a mediator between the user and the tablet. To this end, we propose to use stacking gestures, i.e. laying one edge of a smartphone on the tablet screen. Stacking is a cheap, easy to implement and effective way of employing always-available smartphones to improve data analysis on tablets, by augmenting the input vocabulary and extending the display surface. We first explore stacking gestures to delimitate the possible interaction vocabulary and present the manufacturing of a conductive smartphone case. Then, we propose novel stacking-based techniques to perform spreadsheet data analysis, i.e. pivot table creation and manipulation. We evaluate our stacking techniques against touch interaction as provided by current mobile spreadsheet apps. Our studies reveal that some of our interaction techniques perform 30% faster than touch for creating pivot tables.
Neural Conversation Recommendation with Online Interaction Modeling

The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis.It presents a concrete challenge for individuals to better discover and engage in social media discussions.In this paper, we present a novel framework to automatically recommend conversations to users based on their prior conversation behaviors.Built on neural collaborative filtering, our model explores deep semantic features that measure how a user's preferences match an ongoing conversation's context.Furthermore, to identify salient characteristics from interleaving user interactions, our model incorporates graph-structured networks, where both replying relations and temporal features are encoded as conversation context.Experimental results on two large-scale datasets collected from Twitter and Reddit show that our model yields better performance than previous stateof-the-art models, which only utilize lexical features and ignore past user interactions in the conversations.
First-Order Automated Reasoning with Theories: When Deduction Modulo Theory Meets Practice

We discuss the practical results obtained by the first generation of automated theorem provers based on Deduction modulo theory. In particular, we demonstrate the concrete improvements such a framework can bring to first-order theorem provers with the introduction of a rewrite feature. Deduction modulo theory is an extension of predicate calculus with rewriting both on terms and propositions. It is well suited for proof search in theories because it turns many axioms into rewrite rules. We introduce two automated reasoning systems that have been built to extend other provers with Deduction modulo theory. The first one is Zenon Modulo, a tableau-based tool able to deal with polymorphic first-order logic with equality, while the second one is iProverModulo, a resolution-based system dealing with first-order logic with equality. We also provide some experimental results run on benchmarks that show the beneficial impact of the extension on these two tools and their underlying proof search methods. Finally, we describe the two backends of these systems to the Dedukti universal proof checker, which also relies on Deduction modulo theory, and which allows us to verify the proofs produced by these tools.
Fully invariant generalized Hough transform by out-of-focus multiview sensing with pupil array

Recognition of geometrical shapes in real-time and fully invariant (i.e., invariant under changes in position, scale, and orientation) is a demanding task in automated image analysis. In particular, the generalized Hough transform (GHT) is a well-known algorithm for the recognition of complex patterns out of edge binary images even with disconnected boundaries or corrupted by noise. In this work we present a space multiplexed optical implementation of the GHT which, by exploiting the redundancy derived from multiview sensing of a two-dimensional image and its out- of-focus capture with an adequate pupil array, allows us to obtain in a single shot the GHT of this image invariant to target shift, scale, and orientation. Experimental validation of the working principle is presented, along with an assessment of the robustness of the system against noise in the input.
KeyBoard-Less Online Shopping for the Visually Impaired Using Natural Language Processing and Face Recognition Mechanism

Online shopping has gained popularity for its omnipresence. However, visually impaired people are not able to make complete use of this e-commerce shopping due to lack of user-friendly nature to the visually impaired. Here, in this paper, we have proposed a solution to make the e-commerce websites more user-friendly to the visually impaired using voice-based assistance. Our solution includes Face Recognition technology using OpenCV for login and registration into the e-commerce website. gTTS (Google Text to Speech) and speech_recognition libraries were used for making it completely speech driven. After the search results, to extract the data from the web page Web Scraping was used and the results were stored in the database to analyse the data and to choose the best-rated products. After selection of the product, the product was added to the cart using Selenium Web Driver.
Visual Rationalizations in Deep Reinforcement Learning for Atari Games

Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games. However, clearly explaining why a certain action is taken by the agent can be as important as the decision itself. Deep reinforcement learning models, as other deep learning models, tend to be opaque in their decision-making process. In this work, we propose to make deep reinforcement learning more transparent by visualizing the evidence on which the agent bases its decision. In this work, we emphasize the importance of producing a justification for an observed action, which could be applied to a black-box decision agent.
Unsupervised Domain Adaptation using Graph Transduction Games

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.
Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning

Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants.
Effects of depth, width, and initialization: A convergence analysis of layer-wise training for deep linear neural networks

Deep neural networks have been used in various machine learning applications and achieved tremendous empirical successes. However, training deep neural networks is a challenging task. Many alternatives have been proposed in place of end-to-end back-propagation. Layer-wise training is one of them, which trains a single layer at a time, rather than trains the whole layers simultaneously. In this paper, we study a layer-wise training using a block coordinate gradient descent (BCGD) for deep linear networks. We establish a general convergence analysis of BCGD and found the optimal learning rate, which results in the fastest decrease in the loss. We identify the effects of depth, width, and initialization. When the orthogonal-like initialization is employed, we show that the width of intermediate layers plays no role in gradient-based training beyond a certain threshold. Besides, we found that the use of deep networks could drastically accelerate convergence when it is compared to those of a depth 1 network, even when the computational cost is considered. Numerical examples are provided to justify our theoretical findings and demonstrate the performance of layer-wise training by BCGD.
Research on modern radar emitter modelling technique under complex electromagnetic environment

Since many modern multi-functional radars emitting dense complex signals are applied in the complex electromagnetic environment, it is challenge to classify and identify the radar emitters in the electronic reconnaissance field. In order to classify and identify radar emitters accurately and rapidly, a kind of standard adaptive extended radar emitter modelling structure and the complete process of obtaining radar emitter model parameters are proposed in this study. The simulation analysis results show that the proposed standard modelling structure and complete process can rapidly realise the accurate classification of many multi-functional radar emitters; hence can further benefit to accurate identification of emitters.
HapPenIng: Happen, Predict, Infer—Event Series Completion in a Knowledge Graph

Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such as Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete event series representation. In this paper we target the problem of event series completion in a knowledge graph. We address two tasks: (1) prediction of sub-event relations, and (2) inference of real-world events that happened as a part of event series and are missing in the knowledge graph. To address these problems, our proposed supervised HapPenIng approach leverages structural features of event series. HapPenIng does not require any external knowledge - the characteristics making it unique in the context of event inference. Our experimental evaluation demonstrates that HapPenIng outperforms the baselines by 44 and 52% points in terms of precision for the sub-event prediction and the inference tasks, correspondingly.
Unsupervised Rotation Factorization in Restricted Boltzmann Machines

Finding suitable image representations for the task at hand is critical in computer vision. Different approaches extending the original Restricted Boltzmann Machine (RBM) model have recently been proposed to offer rotation-invariant feature learning. In this paper, we present an extended novel RBM that learns rotation invariant features by explicitly factorizing for rotation nuisance in 2D image inputs within an unsupervised framework. While the goal is to learn invariant features, our model infers an orientation per input image during training, using information related to the reconstruction error. The training process is regularised by a Kullback-Leibler divergence, offering stability and consistency. We used the γ-score, a measure that calculates the amount of invariance, to mathematically and experimentally demonstrate that our approach indeed learns rotation invariant features. We show that our method outperforms the current state-of-the-art RBM approaches for rotation invariant feature learning on three different benchmark datasets, by measuring the performance with the test accuracy of an SVM classifier. Our implementation is available at https://bitbucket.org/tuttoweb/rotinvrbm.
Ambient Intelligence and IoT Based Decision Support System for Intruder Detection

Ambient intelligence is an evolving discipline which brings intelligence to our daily life through various domains comprising elderly assistance, preventive maintenance, video surveillance, etc. It refers to the concept of electronic surroundings that are subtle and reactive to the existence of people or things. In the up-to-date society, criminality reaches out to influence most aspects of people's regular lives. Hence, individuals themselves started assuring against crime. This paper describes an ambient intelligence-based decision support system which provides home-based security. The proposed system mainly focuses on two techniques: face recognition to identify the house holders and anomaly detection to recognize the activities of the individual. Thus it can be used to send alerts to necessary authorities and family members if needed and enables real time monitoring of the captured anomaly through Internet of Things (IoT). The proposed framework produces an efficient face recognition and anomaly detection system with high performance rate.
Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e., fill hole inside objects algorithm. Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm. The classification task is achieved using binary and multi class support vector machines. The proposed technique is validated through accuracy, precision, recall and F-measure metrics.