Reviewerly/exHarmony huggingface dataset:
SensitiveNets: Learning Agnostic Representations with Application to Face Recognition.

This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task.
On the Effectiveness of Low Frequency Perturbations

Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains. In addition, recent work has shown that constraining the attack space to a low frequency regime is particularly effective. Yet, it remains unclear whether this is due to generally constraining the attack search space or specifically removing high frequency components from consideration. By systematically controlling the frequency components of the perturbation, evaluating against the top-placing defense submissions in the NeurIPS 2017 competition, we empirically show that performance improvements in both the white-box and black-box transfer settings are yielded only when low frequency components are preserved. In fact, the defended models based on adversarial training are roughly as vulnerable to low frequency perturbations as undefended models, suggesting that the purported robustness of state-of-the-art ImageNet defenses is reliant upon adversarial perturbations being high frequency in nature. We do find that under $\ell_\infty$ $\epsilon=16/255$, the competition distortion bound, low frequency perturbations are indeed perceptible. This questions the use of the $\ell_\infty$-norm, in particular, as a distortion metric, and, in turn, suggests that explicitly considering the frequency space is promising for learning robust models which better align with human perception.
Learning Multi-Domain Adversarial Neural Networks for Text Classification

Deep neural networks have been applied to learn transferable features for adapting text classification models from a source domain to a target domain. Conventional domain adaptation used to adapt models from an individual specific domain with sufficient labeled data to another individual specific target domain without any (or with little) labeled data. However, in this paradigm, we lose sight of correlation among different domains where common knowledge could be shared to improve the performance of both the source domain and the target domain. Multi-domain learning proposes learning the sharable features from multiple source domains and the target domain. However, previous work mainly focuses on improving the performance of the target domain and lacks the effective mechanism to ensure that the shared feature space is not contaminated by domain-specific features. In this paper, we use an adversarial training strategy and orthogonality constraints to guarantee that the private and shared features do not collide with each other, which can improve the performances of both the source domains and the target domain. The experimental results, on a standard sentiment domain adaptation dataset and a consumption intention identification dataset labeled by us, show that our approach dramatically outperforms state-of-the-art baselines, and it is general enough to be applied to more scenarios.
A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm

Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.
CNN and RNN-based Deep Learning Methods for Digital Signal Demodulation

In this paper we presented a neural network-based method to demodulate digital signals. After training with different modulation schemes, the learning-based receiver can perform demodulation without changing receiver hardware by loading certain parameters based on the modulation scheme. Combining CNN's ability to extract local features and RNN's time series modeling ability, we designed a mixed neural network model with parallel architecture and simulate FSK, PSK, QAM demodulation over AWGN and Raleigh-faded channels. The results show that the mixed neural network model can equal or even exceed the performance of the conventional demodulation method (matched filter or correlation-based demodulation). With this kind of receiver, we can intelligently process multiple types of digital modulated signals with flexibility.
Frequency regulation service provision in data center with computational flexibility

The rapid adoption of cloud storage and computing services led to unprecedented growth of data centers in the world. As bulk energy consumers, large-scale data centers in the U.S. rack up billions in electricity costs annually. Fortunately, the operational flexibility of data centers can be leveraged to provide valuable frequency regulation services in smart grids to mitigate the indeterminacy of the renewable generation resources. Specifically, this paper aims to leverage computational flexibility provided by servers, such as dynamic voltage frequency scaling and dummy loads. This paper develops a comprehensive framework for data center’s frequency regulation service provision in both hour-ahead market and real-time operations. A risk constrained hour-ahead bidding strategy along with a real-time data center power consumption control algorithm are developed to minimize electricity bills and the total response time of the requests. The introduction of dummy load, realistic bi-linear server power consumption model, and probabilistic forecast of electricity and frequency regulation service prices enable the data center to accurately follow frequency regulation signals, while reducing the financial risks associated with electricity market participation. The simulation results show that the proposed frequency regulation provision framework results not only in significant cost reduction for data centers, but also limits degradation in quality of service. Meanwhile, the stability and reliability of a power grid will be improved by the frequency regulation service provision.
Vector of Locally-Aggregated Word Embeddings (

Radu Tudor Ionescu, Andrei Butnaru. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Quantum Rabi–Stark model: solutions and exotic energy spectra

The quantum Rabi–Stark model, where the linear dipole coupling and the nonlinear Stark-like coupling are present on an equal footing, are studied within the Bogoliubov operators approach. Transcendental functions responsible for the exact solutions are derived in a compact way, much simpler than previous ones obtained in the Bargmann representation. The zeros of transcendental functions reproduce completely the regular spectra. In terms of the explicit pole structure of these functions, two kinds of exceptional eigenvalues are obtained and distinguished in a transparent manner. Very interestingly, a first-order quantum phase transition indicated by level crossing of the ground state and the first excited state is induced by the positive nonlinear Stark-like coupling, which is however absent in any previous isotropic quantum Rabi models. When the absolute value of the nonlinear coupling strength is equal to twice the cavity frequency, this model can be reduced to an effective quantum harmonic oscillator, and solutions are then obtained analytically. The spectra collapse phenomenon is observed at a critical coupling, while below this critical coupling, infinite discrete spectra accumulate into a finite energy from below.
UESTS: An Unsupervised Ensemble Semantic Textual Similarity Method

Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning.Several approaches have been proposed in the literature to determine the semantic similarity between texts.The most promising work recently presented in the literature was supervised approaches.Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations.Word alignment has been widely used in the state-ofthe-art approaches.From this point, this paper has three contributions.First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet.Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL).Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach.The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance.The experimental results proved that the participation of the proposed aligner in STS is effective.Over all the evaluation data sets, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem. Bits-Back with Asymmetric Numeral Systems (BB-ANS), recently proposed by Townsend et al. (2019), makes bits-back coding practically feasible for latent variable models with one latent layer, but it is inefficient for hierarchical latent variable models. In this paper we propose Bit-Swap, a new compression scheme that generalizes BB-ANS and achieves strictly better compression rates for hierarchical latent variable models with Markov chain structure. Through experiments we verify that Bit-Swap results in lossless compression rates that are empirically superior to existing techniques. Our implementation is available at this https URL.
Gated and attentive neural collaborative filtering for user generated list recommendation

Recommending user generated lists (e.g., playlists) has become an emerging task in many online systems. Many existing list recommendation methods predict user preferences on lists by aggregating their preferences on individual items, which neglects the list-level information (e.g., list attributes) and thus results in suboptimal performance. This paper proposes a neural network-based solution for user generated list recommendation, which can leverage both item-level information and list-level information to improve performance. Firstly, a representation learning network with attention and gate mechanism is proposed to learn the user embeddings, item embeddings and list embeddings simultaneously. Then, an interaction network is proposed to learn user–item interactions and user–list interactions, in which the two kinds of interactions can share the convolution layers to further improve performance. Experimental studies on two real-world datasets demonstrate that (1) the proposed representation learning network can learn more representative user/item/list embedding than existing methods and (2) the proposed solution can outperform state-of-the-art methods in both item recommendation and list recommendation in terms of accuracy.
ONLINE FORUMS: COMMUNICATION MODEL, CATEGORIES OF ONLINE COMMUNICATION REGULATION AND NORMS OF BEHAVIOR

Purpose of the study: The paper addresses the issue of regulating Internet communication activities in the context of online forums. The study aims to create a forum communication model identifying the areas subject to social regulation and defining the most common types of norms&#x0D; Methodology: The study draws upon the semiotic method to develop a new communication model based on classical theories. Quantitative empirical studies are also used. The rules and guidelines of 1,205 online forums were analyzed to identify the most common norms.&#x0D; Main Findings: Communicators and moderators access the forum communication space via user accounts and create a sign system. Some categories, such as a ban on deceptive or offensive content, are similar to those used in offline life. Other regulations are Internet-specific in that they are determined by inherent challenges of online communication (for example, account cloning) or by the way a forum is structured.&#x0D; Applications of this study: The findings related to the forum rules are presented in a summarized manner, although the rules vary slightly in reality, depending on the forum topic. The prevalence of deviant behaviors on online forums was also beyond the scope of this study. The study was based on the rules of Russian- and English-speaking forums, and it should be noted that language differences could influence the results.&#x0D; Social Implications: The evolving social interactions on the Web require regulations, which can differ significantly from those used offline. The study of social norms on the Internet allows us to trace what types of online behavior are now considered deviant and examine the current potential of Internet self-regulation.&#x0D; Novelty/Originality of this study: The study developed a new communication model for categorizing online communication norms. Major categories of online communication behavior regulation were identified and the most popular types of social norms were determined.
Using neural network for credit card fraud detection

In the present time, the internet has become more popular in almost every domain of life. Online shopping, e-commerce and transactions are increasing every day. Fraudulent activities have also increased in the online payment system. Payment card fraud has become a serious problem in the world. Companies and institutions lose huge amounts annually due to fraud and the fraudsters continuously seek new ways to commit illegal actions. The good news is that fraud tends to be perpetrated to certain patterns and that it is possible to detect such patterns, and hence fraud. In this paper, we present a way to detect fraudulent transactions by a neural network model. Artificial neural network (ANN), when trained properly can work like a human brain. They learn by example, like people and are known as a very good classifier. Among the main characteristics of credit card traffic are the great imbalance between proper and fraudulent operations. At the same time, public data are hardly available for confidentiality issues. To deal with the imbalanced dataset we use resampling techniques. To ensure proper model construction we made a pattern recognition network trained with a scaled conjugate gradient backpropagation algorithm using the Neural network toolbox in Matlab.
Estimating Video Streaming QoE in the 5G Architecture Using Machine Learning

Compared to earlier mobile network generations, the 5G system architecture has been significantly enhanced by the introduction of network analytics functionalities and ex- tended capabilities of interacting with third party Application Functions (AFs). Combining these capabilities, new features for Quality of Experience (QoE) estimation can be designed and introduced in next generation networks. It is, however, unclear how 5G networks can collect monitoring data and application metrics, how they correlate to each other, and which techniques can be used in 5G systems for QoE estimation. This paper studies the feasibility of Machine Learning (ML) techniques for QoE estimation and evaluates their performance for a mobile video streaming use-case. A simulator has been implemented with OMNeT++ for generating traces to (i) examine the relevance of features generated from 5G monitoring data and (ii) to study the QoE estimation accuracy (iii) for a variable number of used features.
Instantaneous thickness measurement of multilayer films by single-shot angle-resolved spectral reflectometry

In this Letter, we describe a new, to the best of our knowledge, concept of angle-resolved spectral reflectometry with a pixelated polarizing camera for determination of the thickness of each film layer in multilayer films. We can measure the changes in the phase and amplitude of p- and s-polarized light over a broad spectral range and a wide incident angle at a time. The proposed method is verified by measuring a sample of multilayer film and comparing our measurement results with an ellipsometer. The comparison results show that our proposed technique enables real-time inspection of multilayer films with high precision.
Scalable Private Set Union from Symmetric-Key Techniques

We present a new efficient protocol for computing private set union (PSU). Here two semi-honest parties, each holding a dataset of known size (or of a known upper bound), wish to compute the union of their sets without revealing anything else to either party. Our protocol is in the OT hybrid model. Beyond OT extension, it is fully based on symmetric-key primitives. We motivate the PSU primitive by its direct application to network security and other areas. At the technical core of our PSU construction is the reverse private membership test (RPMT) protocol. In RPMT, the sender with input $$x^*$$ interacts with a receiver holding a set X. As a result, the receiver learns (only) the bit indicating whether $$x^* \in X$$, while the sender learns nothing about the set X. (Previous similar protocols provide output to the opposite party, hence the term “reverse” private membership.) We believe our RPMT abstraction and constructions may be a building block in other applications as well. We demonstrate the practicality of our proposed protocol with an implementation. For input sets of size $$2^{20}$$ and using a single thread, our protocol requires 238 s to securely compute the set union, regardless of the bit length of the items. Our protocol is amenable to parallelization. Increasing the number of threads from 1 to 32, our protocol requires only 13.1 s, a factor of $$18.25{\times }$$ improvement. To the best of our knowledge, ours is the first protocol that reports on large-size experiments, makes code available, and avoids extensive use of computationally expensive public-key operations. (No PSU code is publicly available for prior work, and the only prior symmetric-key-based work reports on small experiments and focuses on the simpler 3-party, 1-corruption setting.) Our work improves reported PSU state of the art by factor up to $$7,600{\times }$$ for large instances.
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks

Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train compared to standard deep neural networks. In this paper, we propose new visualization techniques for the optimization landscapes of GANs that enable us to study the game vector field resulting from the concatenation of the gradient of both players. Using these visualization techniques we try to bridge the gap between theory and practice by showing empirically that the training of GANs exhibits significant rotations around LSSP, similar to the one predicted by theory on toy examples. Moreover, we provide empirical evidence that GAN training seems to converge to a stable stationary point which is a saddle point for the generator loss, not a minimum, while still achieving excellent performance.
Unsupervised Deep Feature Transfer for Low Resolution Image Classification

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high-and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.
The Next Generation of Social Products Based on Sensing, Smart and Sustainable (S3) Features: A Smart Thermostat as Case Study

Since Internet of Things (IoT), Machine Learning, Digital Systems, Control Systems were incorporated in product design, a new designing technology era raised up so the conventional methods for developing products could not be valid anymore since they do not include social features that allow interacting with consumers and other products. Sometimes, the products are extremely limited in the social communication between products to consumers and products to products, and the primary communication system is the interface, but typically, this interface is not designed as a social element. As a result, social communication is minimal. Thus, a basic design methodology that allows product designers to incorporate social factors between products to consumers and products to products is presented in this paper. Also, the term social communication PCPP (Product-Consumer and Product-Product) is coined as a part of the product design methodology that allows defining the social features as primary elements in the process of designing products. Besides, a conceptual method for smart thermostats is proposed for a social communication PCPP to demonstrate how this conceptual proposal could be implemented using the sensing, smart and sustainable (S3) features.