Reviewerly/exHarmony huggingface dataset:
Curriculum-based Teacher Ensemble for Robust Neural Network Distillation

Neural network distillation is used for transferring the knowledge from a complex teacher network into a lightweight student network, improving in this way the performance of the student network. However, neural distillation does not always lead to consistent results, with several factors affecting the efficiency of the knowledge distillation process. In this paper it is experimentally demonstrated that the selected teacher can indeed have a significant effect on knowledge transfer. To overcome this limitation, we propose a curriculum-based teacher ensemble that allows for performing robust and efficient knowledge distillation. The proposed method is motivated by the way that humans learn through a curriculum, as well as supported by recent findings that hints to the existence of critical learning periods in neural networks. The effectiveness of the proposed approach, compared to various distillation variants, is demonstrated using three image datasets and different network architectures.
Jpg$Scraper$ : An Advanced Carver for JPEG Files

Orphaned file fragment carving is concerned with recovering contents of encoded data in the absence of any coding metadata. Constructing an orphaned file carver requires addressing three challenges: a specialized decoder to interpret partial file data; the ability to discriminate a specific type of encoded data from all other types of data; and comprehensive prior knowledge on possible encoding settings. In this work, we build on the ability to render a partial image contained within a segment of JPEG coded data to introduce a new carving tool that addresses all these challenges. Towards this goal, we first propose a new method that discriminates JPEG file data from among 993 file data types with 97.7% accuracy. We also introduce a method for robustly delimiting entropy coded data segments of JPEG files. This in turn allows us to identify partial JPEG file headers with zero false rejection and 0.1% of false alarm rate. Secondly, we examine a very diverse image set comprising more than 7 million images. This ensures comprehensive coverage of coding parameters used by 3,269 camera models and a wide variety of image editing tools. Further, we assess the potential impact of the developed tool on practice in terms of the amount of new evidence that it can recover. Recovery results on a set of used SD cards purchased online show that our carver is able to recover 24% more image data as compared to existing file carving tools. Evaluations performed on a standard dataset also show that JpgScraper improves the state-of-the-art significantly in carving JPEG file data.
Kernel Optimized-Support Vector Machine and MapReduce Framework for Sentiment Classification of Train Reviews

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.
Asymptotic Gilbert–Varshamov Bound on Frequency Hopping Sequences

Given a q-ary frequency hopping sequence set of length n and size M with Hamming correlation H, one can obtain a q-ary (nonlinear) cyclic code of length n and size nM with Hamming distance n-H. Thus, every upper bound on the size of a code from coding theory gives an upper bound on the size of a frequency hopping sequence set. Indeed, all upper bounds from coding theory have been converted to upper bounds on frequency hopping sequence sets [1]. On the other hand, a lower bound from coding theory does not automatically produce a lower bound for frequency hopping sequence sets. In particular, the most important lower bound, the Gilbert-Varshamov bound in coding theory, has not been transformed to a valid lower bound on frequency hopping sequence sets. The purpose of this paper is to transform the Gilbert-Varshamov bound from coding theory to frequency hopping sequence sets by establishing a connection between a special family of cyclic codes (which are called hopping cyclic codes in this paper) and frequency hopping sequence sets. We provide two proofs of the Gilbert-Varshamov bound. One is based on a probabilistic method that requires advanced tool- martingale. This proof covers the whole rate region. Another proof is purely elementary but only covers part of the rate region.
Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices

Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality.Our approach is to decompose the covariance matrix into the correlation and variance matrices and propose a novel Bayesian framework based on modeling the correlations as products of unit vectors.By specifying a wide range of distributions on a sphere (e.g. the squared-Dirichlet distribution), the proposed approach induces flexible prior distributions for covariance matrices (that go beyond the commonly used inverse-Wishart prior).For modeling real-life spatio-temporal processes with complex dependence structures, we extend our method to dynamic cases and introduce unit-vector Gaussian process priors in order to capture the evolution of correlation among components of a multivariate time series.To handle the intractability of the resulting posterior, we introduce the adaptive Δ-Spherical Hamiltonian Monte Carlo.We demonstrate the validity and flexibility of our proposed framework in a simulation study of periodic processes and an analysis of rat's local field potential activity in a complex sequence memory task.
Neural-network-based Memory for a Social Robot

Many recent studies have shown that behaviors and interaction logic for social robots can be learned automatically from natural examples of human-human interaction by machine learning algorithms, with minimal input from human designers [1--4]. In this work, we exceed the capabilities of the previous approaches by giving the robot memory . In earlier work, the robot's actions were decided based only on a narrow temporal window of the current interaction context. However, human behaviors often depend on more temporally distant events in the interaction history. Thus, we raise the question of whether (and how) an automated behavior learning system can learn a memory representation of interaction history within a simulated camera shop scenario. An analysis of the types of memory-setting and memory-dependent actions that occur in the camera shop scenario is presented. Then, to create more examples of such actions for evaluating a shopkeeper robot behavior learning system, an interaction dataset is simulated. A Gated Recurrent Unit (GRU) neural network architecture is applied in the behavior learning system, which learns a memory representation for performing memory-dependent actions. In an offline evaluation, the GRU system significantly outperformed a without-memory baseline system at generating appropriate memory-dependent actions. Finally, an analysis of the GRU architecture's memory representation is presented.
Intertwined Development of Business Model and Product Functions for Mobile Applications: A Twin Peak Feature Modeling Approach

Mobile app stores like Apple's AppStore or Google's PlayStore are highly competitive markets for third-party developers wanting to develop successful applications. During the development process, many developers focus on the multitude of product functions but neglect the business model as an equally important part. As a result, developers often fail to meet customer needs, leading to unnecessary development costs and poor market penetration. This, in turn, raises the question of how we intertwine the business model and product functions during the development process to ensure a better alignment between the two. In this paper, we show this intertwined development by adapting the concept of Twin Peaks to the business model and product functions. Based on feature modeling as an abstraction layer, we introduce the concept of a Business Model Decision Line (BMDL) to structure the business model decisions and their relation to product functions structured in a Software Product Line (SPL). The basis of our feature models is the analysis of top listed applications in the app stores of Apple and Google. To create and modify both models, we provide an incremental feature structuring and iterative feature selection process. This combination of abstraction layer and development process supports third-party developers to build successful applications both from a business and a product perspective.
Proposal and Definition of a Methodology for Remote Detection and Prevention of Hypoxemic Clinical Cases in Patients Susceptible to Respiratory Diseases

Respiratory diseases are nowadays among the main causes of mortality and disability worldwide, and an intense effort has been made in the last years towards their prevention and early detection. Many of the proposed methods are based on the concept of remote medicine or 'telemedicine', a concept that encompasses a collection of techniques based on the remote care and assistance to patients through the utilization of networks and mobile devices. In this context, the current work presents the proposal and development of a methodology for the remote detection and prevention of potential hypoxemic cases in patients susceptible to respiratory diseases. Such methodology makes use of an expert system capable of generating alerts about the degree of hypoxemia of a patient, from a data set coming from both physical measurements and professional considerations. Protocols are defined for the data collection and its treatment using mobile terminals with sensors connected to them. Additionally, the collected data is stored and processed by fuzzy-logic inference systems as a support to the decision process that is the previous step to the generation of the final alert, named 'Global Hypoxemic Risk'. This new methodology has been tested experimentally and the results have been positive, resulting in a substantial reduction in the time for the detection of the oxygen deficit, and in its further treatment and prevention of physiological consequences.
Toward Undetectable Quantum Key Distribution Over Bosonic Channels

We propose a protocol based on pulse-position modulation and multi-level coding that allows one to bootstrap traditional quantum key distribution protocols while ensuring covertness, in the sense that no statistical test by the adversary can detect the presence of communication over the quantum channel better than a random guess. When run over a bosonic channel, our protocol can leverage existing discrete-modulated continuous-variable protocols. Since existing techniques to bound Eve's information do not directly apply, we develop a new bound that results in positive, although very low, throughput for a range of channel parameters. The analysis of the protocol performance shows that covert secret key expansion is possible using a public authenticated classical channel and a quantum channel largely but not fully under the control of an adversary, which we precisely define. We also establish a converse result showing that, under the golden standard of quantum key distribution, by which the adversary completely controls the quantum channel, no covert key generation is possible.
Secure Data Transmission Through Steganography with Blowfish Algorithm

The Steganography is a strategy for thumping mystery communications in a shelter protest while correspondence happens among dispatcher and collector. Safety of mystery or vital data has dependably be noteworthy issues after the previous occasions to the right period. It will be dependably with the attentive subject for specialists to create secured methods to sends information deprived of uncovering it to anybody further than the collector. In this way from everyday analysts have created numerous methods to satisfy secure exchange of information with Steganography is one of them. Here we have built up another method of photo Steganography privileged the inserting the encoded Data document or information utilizing RSA algorithm with Hash-LSB for giving greater safety to information with in addition this information hiding strategy. The created technique utilizes a hash capacity to produce an example for hide information bits into LSB of RGB pixel estimations of the convey images. This method ensures with the data-set has been encoded before implanting it into a convey image. Implanted content in images generally conveys vital message around the substance, in the event that the any cases outsider get the message such a significant number of ways, so keep this activity this paper actualizes hash table encryption to the message at that point cover up addicted to the images motive is to give extra secured approach to exchange information. This work is another method for hide the data in a photo and minimum variety in the photo bits has been made, that create this method secured with more proficient. In the method additionally connected a cryptography strategy. Other stage is to encode and unscramble stenographic images utilizing blowfish algorithm, this activity utilized to deal with other series of security procedure usage.
SnapChart: an Augmented Reality Analytics Toolkit to Enhance Interactivity in a Collaborative Environment

Collaborative Immersive Analytics (IA) is a tool which enables multiple people to explore the same dataset using immersive technologies, such as Augmented Reality (AR) or Virtual Reality (VR). In this poster, we describe a system which uses AR to provide situated 3D visualisations in a practical agile collaborative setting. Through a preliminary user study we found that our system helps users accept the concept of IA while enhancing engagement and interactivity during AR collaboration.
Virtual Fakes: DeepFakes for Virtual Reality

The proliferation of data and computational resources has led into many advancements in computer vision for facial data including easily replacing a face in one video with another one, the so called DeepFake. In this paper, we apply techniques to generate DeepFakes for virtual reality applications. We empirically validate our method by generating, for the first time, Deep Fake videos in virtual reality.
You Don't Need a Ledger

Centralized systems relying on a trusted third party are being replaced by decentralized systems using proof-of-work blockchains to reach consensus between multiple mistrusting parties. Due to the high energy usage of such systems, many solutions using a Byzantine fault-tolerant algorithm to reach consensus have emerged. While those systems solve the energy and safety concerns of proof-of-work blockchains, they still require a peer to store the full ledger and need a complex backend infrastructure to get started.
Indicator mineral and till geochemical signatures of the Broken Hammer Cu–Ni–PGE–Au deposit, North Range, Sudbury Structure, Ontario, Canada

The Broken Hammer Cu–Ni–PGE–Au footwall deposit in the North Range of the Sudbury Structure in Canada consists of a shallow surface zone of vein-hosted and vein stockwork-hosted mineralization within Sudbury breccia developed in the quartz monzonite Levack Gneiss Complex. The surface of the deposit consists of a 2–120 cm wide chalcopyrite vein and numerous smaller veins dominated by chalcopyrite–magnetite–millerite with trace gold, platinum group minerals, tellurides, bismuthides and selenides. The Laurentide Ice Sheet flowed southward across the region depositing a sandy till that contains abundant sperrylite (hundreds of grains), chalcopyrite, pyrite and gold in the heavy mineral fraction down-ice of mineralization. Mineral liberation analysis of the &lt;0.25 mm heavy mineral fraction of metal-rich till identified a broader suite of PGM and sulfides than visual identification methods. The &lt;0.063 mm fraction of till displays a strong geochemical signature of the mineralization for Pd, Pt, Au, Cu and Ag and, to a lesser extent, Bi, Te and Sn; however, geochemical signatures are not detectable as far down-ice as indicator minerals. Till sampling has not been used for exploration in the Sudbury region because of the abundant outcrop and the use of geophysical and prospecting techniques. This study demonstrates that indicator mineral and till geochemical methods are useful exploration tools for the region. The presence of sperrylite and chalcopyrite in oxidized till indicates that even thin (&lt;1 m) highly weathered till is an effective sample medium here.
A real-time spectral analysis method and its FPGA implementation for long-sequence signals

Failure of important mechanical equipment poses a significant threat to the production line and personal safety. Real-time spectrum information is a critical indicator of the operating state of an equipment. Spectrum measurement equipment requires fast and accurate acquisition of spectral values of facilities to obtain real-time spectrum. A Blocking FFT algorithm is proposed in this paper to improve the real-time performance when calculating frequency spectrum of long sequence signals in embedded spectrum measurement devices. The signal points are divided into consecutive time blocks. Each block is calculated separately in the Blocking FFT algorithm, and the spectrum value is obtained by superimposing the results of each block. The algorithm is tested with Xilinx ZYNQ embedded devices to verify the actual performance. The Blocking FFT algorithm is compared with the traditional FFT in terms of numerical accuracy, speed and resource consumption. Experimental results demonstrate that the real-time performance of the algorithm is better than that of traditional FFT algorithm. The Blocking FFT algorithm allows long sequence signal data spectral calculations to be performed on devices with fewer resources within a certain sampling frequency. The Blocking FFT algorithm also provides a stable and efficient solution for super-long sequence signal data spectral analysis.
Deep Log-Likelihood Ratio Quantization

In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting. A deep autoencoder network is trained to compress, quantize and reconstruct the bit log-likelihood ratios corresponding to a single transmitted symbol. Specifically, the encoder maps to a latent space with dimension equal to the number of sufficient statistics required to recover the inputs - equal to three in this case - while the decoder aims to reconstruct a noisy version of the latent representation with the purpose of modeling quantization effects in a differentiable way. Simulation results show that, when applied to a standard rate-1/2 low-density parity-check (LDPC) code, a finite precision compression factor of nearly three times is achieved when storing an entire codeword, with an incurred loss of performance lower than 0.15 dB compared to straightforward scalar quantization of the log-likelihood ratios and the method is competitive with state-of-the-art approaches.
Sexual Harassment Story Classification and Key Information Identification

Recently more and more personal stories about sexual harassment are shared online, mainly inspired by the \#MeToo movement. Safecity is an online forum for victims of sexual harassment to share their personal experience. Previous study applied neural network models to classify the harassment forms of the stories. To uncover patterns of sexual harassment, the extraction of the key elements and the categorization of these stories in different dimensions can be useful as well. In this study, we proposed neural network models to extract key elements including harasser, time, location and trigger words. In addition, we categorized these stories from different dimensions, such as location, time, and harassers' characteristics, including their age range, single/multiple harassers, profession, and relationship with the victims. We further demonstrated that encoding the key element information in the story categorization model can improve its performance. The proposed approaches and analysis would be helpful in automatically filing reports, raising public awareness, making preventing strategies and etc.
Hate Speech in Spain Against Aquarius Refugees 2018 in Twitter

High-profile events can trigger online hate speech, which in turn modify attitudes and offline behavior towards stigmatized groups. This paper addresses the first path of this process by using manual and computational methods to analyze the complete stream of Twitter messages in Spanish referring the boat Aquarius (N = 24,254) From the rejection of Italy, until the arrival at the Spanish port of Valencia, which was a milestone for the entry of refugees and was highly covered by the media. We found that most of the messages revolved around few topics and were mostly positive, but a significant part of negative messages included hate speech towards refugees and rejection of politicians. Supporting our hypothesis, online hate speech grew after the announcement. The general positive sentiment paradoxically increased, and the language sentiment became less negative. We discuss the theoretical and practical implications, and acknowledge limitations referred to the examined timeframe, suggesting more longitudinal analyses.
Interaction Graph Neural Network for News Recommendation

Personalized news recommendation has become a highly challenging problem in recent years. Traditional ID-based methods such as collaborative filtering are not suitable for news recommendation due to the extremely rapid update of candidate news. Various content-based methods have been proposed for news recommendation and achieved the state-of-the-art performance. Recently, knowledge-aware news recommendation further improves the performance through discover latent knowledge level connections among the news. However, we argue that the above content-based methods do not fully utilize the collaborative information latent in user-item interactions into user and news representation learning process. In this paper, we propose a new news recommendation model, Interaction Graph Neural Network (IGNN), which integrates a user-item interactions graph and a knowledge graph into the news recommendation model. Specifically, IGNN obtains the representation of users and items with two graphs. One is the knowledge graph, and another is the user-item interaction graph. It learns the content-based feature from knowledge-level and semantic-level with convolutional neural networks and fuses the high-order collaborative signals extracted from the user-item interaction graph into user and news representation learning process with a graph neural network. Extensive experiments are conducted on the two real-world news data sets, and experimental results show that IGNN significantly outperforms the state-of-the-art approaches for news recommendation.