Reviewerly/exHarmony huggingface dataset:
Detecting shilling attacks in recommender systems based on analysis of user rating behavior

The existing unsupervised methods for detecting shilling attacks are mostly based on the rating patterns of users, ignoring the rating behavior difference between genuine users and attack users, and these methods suffer from low accuracy in detecting various shilling attacks without a priori knowledge of the attacks. To address these limitations, we propose a novel unsupervised shilling attack detection model based on an analysis of user rating behavior. First, we identify the target item(s) and the corresponding intentions of the attack users by analyzing the deviation of rating tendencies on each item, and based on this analysis, a set of suspicious users is constructed. Second, we analyze the users' rating behaviors from an interest preference and rating preference perspective. In particular, we measure the diversity and memory of users' interest preferences by entropy and block entropy, respectively, and we analyze the memory of user rating preferences by a self-correlation analysis. Finally, we calculate the suspicious degree and spot attack users in the set of suspicious users based on measurements of user rating behavior. Experimental results on the Netflix dataset, the MovieLens 1M dataset and the sampled Amazon review dataset demonstrate the effectiveness of the proposed detection model.
Semi-supervised End-to-end Speech Recognition Using Text-to-speech and Autoencoders

We introduce speech and text autoencoders that share encoders and decoders with an automatic speech recognition (ASR) model to improve ASR performance with large speech only and text only training datasets. To build the speech and text autoencoders, we leverage state-of-the-art ASR and text-to-speech (TTS) encoder decoder architectures. These autoencoders learn features from speech only and text only datasets by switching the encoders and decoders used in the ASR and TTS models. Simultaneously, they aim to encode features to be compatible with ASR and TTS models by a multi-task loss. Additionally, we anticipate that TTS joint training can also improve the ASR performance because both ASR and TTS models learn transformations between speech and text. The experimental result we obtained with our semi-supervised end-to-end ASR/TTS training revealed reductions from a model initially trained with a small paired subset of the LibriSpeech corpus in the character error rate from 10.4% to 8.4% and word error rate from 20.6% to 18.0% by retraining the model with a large unpaired subset of the corpus.
Enhanced Magnetic Sensitivity with Non-Gaussian Quantum Fluctuations

The precision of a quantum sensor can overcome its classical counterpart when its constituents are entangled. In Gaussian squeezed states, quantum correlations lead to a reduction of the quantum projection noise below the shot noise limit. However, the most sensitive states involve complex non-Gaussian quantum fluctuations, making the required measurement protocol challenging. Here we measure the sensitivity of nonclassical states of the electronic spin $J=8$ of dysprosium atoms, created using light-induced nonlinear spin coupling. Magnetic sublevel resolution enables us to reach the optimal sensitivity of non-Gaussian (oversqueezed) states, well above the capability of squeezed states and about half the Heisenberg limit.
Enhancing Texture Perception in Virtual Reality Using 3D-Printed Hair Structures

Experiencing materials in virtual reality (VR) is enhanced by combining visual and haptic feedback. While VR easily allows changes to visual appearances, modifying haptic impressions remains challenging. Existing passive haptic techniques require access to a large set of tangible proxies. To reduce the number of physical representations, we look towards fabrication to create more versatile counterparts. In a user study, 3D-printed hairs with length varying in steps of 2.5 mm were used to influence the feeling of roughness and hardness. By overlaying fabricated hair with visual textures, the resolution of the user's haptic perception increased. As changing haptic sensations are able to elicit perceptual switches, our approach can extend a limited set of textures to a much broader set of material impressions. Our results give insights into the effectiveness of 3D-printed hair for enhancing texture perception in VR.
Multi modal spatio temporal co-trained CNNs with single modal testing on RGB–D based sign language gesture recognition

Extracting hand movements using single RGB video camera for sign language recognition is a necessary attribute in developing an automated sign language recognition system. Local spatio temporal methods has shown encouraging outcomes for hand extraction using color cues. However, the color intensities does not behave as an independent entity during video capture in real environments. This has become a roadblock in the development of sign language machine translator for processing video data in real world environments. Not surprisingly, the result is more accurate when additional information is provided in the form of depth for sign language recognition in real environments. In this paper, we make use of a multi modal feature sharing mechanism with a four-stream convolutional neural network (CNNs) for RGB – D based sign language recognition. Unlike the multi stream CNNs, where output class prediction is based on independently operated two or three modal streams due to scale variations, we propose a feature sharing multi stream CNN on multi modal data for sign language recognition. The proposed 4 – stream CNN divides into two input data groupings under the training and testing spaces. The training space uses four inputs: RGB spatial in main stream and depth spatial, RGB and depth temporal on Region of interest mapping (ROIM) stream. The testing space uses only RGB and RGB temporal data for prediction from the trained model. The ROIM stream shares the multi modal data to generate ROI maps of the human subject, which are used to regulate the feature maps in RGB stream. The scale variations in the three streams is managed by translating the depth map to fit the RGB data. Sharing of multi modal features with RGB spatial features during training has circumvented overfitting on RGB video data. To validate the proposed CNN architecture, the accuracy of the classifier is investigated with RGB-D sign language data and three benchmark action datasets. The results show a remarkable behaviour of the classifier in handling missing depth modal during testing. The robustness of the system against state – of – the – art action recognition methods is studied using contrasting datasets.
Differentially Private Malicious Agent Avoidance in Multiagent Advising Learning

Agent advising is one of the key approaches to improve agent learning performance by enabling agents to ask for advice between each other. Existing agent advising approaches have two limitations. The first limitation is that all the agents in a system are assumed to be friendly and cooperative. However, in the real world, malicious agents may exist and provide false advice to hinder the learning performance of other agents. The second limitation is that the analysis of communication overhead in these approaches is either overlooked or simplified. However, in communication-constrained environments, communication overhead has to be carefully considered. To overcome the two limitations, this paper proposes a novel differentially private agent advising approach. Our approach employs the Laplace mechanism to add noise on the rewards used by student agents to select teacher agents. By using the differential privacy technique, the proposed approach can reduce the impact of malicious agents without identifying them. Also, by adopting the privacy budget concept, the proposed approach can naturally control communication overhead. The experimental results demonstrate the effectiveness of the proposed approach.
Ring Signatures: Logarithmic-Size, No Setup—from Standard Assumptions

Ring signatures allow for creating signatures on behalf of an ad hoc group of signers, hiding the true identity of the signer among the group. A natural goal is to construct a ring signature scheme for which the signature size is short in the number of ring members. Moreover, such a construction should not rely on a trusted setup and be proven secure under falsifiable standard assumptions. Despite many years of research this question is still open. In this paper, we present the first construction of size-optimal ring signatures which do not rely on a trusted setup or the random oracle heuristic. Specifically, our scheme can be instantiated from standard assumptions and the size of signatures grows only logarithmically in the number of ring members. We also extend our techniques to the setting of linkable ring signatures, where signatures created using the same signing key can be linked.
Printer Pals

Whereas there have been significant improvements in the quality of care provided for people with dementia, limited attention to the importance for people with dementia being enabled to make positive social contributions within care home contexts can restrict their sense of agency. In this paper we describe the design and deployment of 'Printer Pals' a receipt-based print media device, which encourages social contribution and agency within a care home environment. The design followed a two-year ethnography, from which the need for highlighting participation and supporting agency for residents within the care home became clear. The residents use of Printer Pals mediated participation in a number of different ways, such as engaging with the technology itself, offering shared experiences and participating in co-constructive and meaningful ways, each of which is discussed. We conclude with a series of design consideration to support agentic and caring interactions through inclusive design practices.
Information-theoretic approaches to privacy in estimation and control

Network control systems (NCSs) heavily rely on information and communication technologies for sharing information between sensors and controllers as well as controllers and actuators. When estimation, control or actuation tasks in a NCS are performed by an untrusted party, sharing information might result in the leakage of private information. The current paper reviews some of the recent results on the privacy-aware decision-making problems in NCSs. In particular, we focus on static and dynamic decision-making problems wherein privacy is measured using information-theoretic notions. We also review the applications of these problems in smart buildings and smart grids.
Pedology and soil class mapping from proximal and remote sensed data

Pedological assessment and mapping by hyperspectral proximal and multispectral remote sensing comes up as an important alternative for large extent areas with high soil variability. Strategies indicating how to integrate these multi-source data for digital soil mapping are emerging. In this paper, we used proximal and remote sensed data to perform pedological assessments and to support a detailed soil class mapping by MESMA. The study area is located in Southeast of Federal District, Brazil. Six toposequences were defined according to pedomorphogeological assessments and 34 sites were collected for laboratory analysis (texture and chemical) and VIS-NIR-SWIR (350–2500 nm) spectroscopy reflectance analysis. We assessed soil mineralogy based on derivative analysis of topsoil reflectance and we grouped observations to recognize topsoil spectral patterns by clustering method. Topsoil patterns (mean spectral curve for each cluster) were convolved using a Gaussian function of Landsat 5-TM spectral bands to obtain endmembers. Then, we used a Landsat 5 TM time series to produce a bare soil composite denominated Synthetic Soil Image (SYSI). Endmembers and SYSI were used as input data for Multiple Endmember Spectral Mixture Analysis (MESMA) to map the soil classes. Topsoil spectra clustered soil samples that were similar in texture, mineralogy and color, and identified 13 topsoil patterns (endmembers). SYSI retrieved 74% of bare soil area coverage and presented very similar spectral patterns to endmembers. The RGB 543 composite highlighted the mineralogy (i.g. sesquioxides, kaolinite and quartz), texture and color of the soils. MESMA modeled almost 100% of SYSI from endmembers, with low global RMSE of 0.86% and high global fraction of 62%. Soil classes were mapped using topsoil reflectance patterns and satellite images by MESMA method. We validated the digital soil map by using independent field-visited sites, which reached a Kappa coefficient of 0.73. We efficiently assessed soil mineralogy and recognized patterns from laboratory topsoil spectra. We successfully mapped soil classes using topsoil reflectance patterns and a bare soil composite image by MESMA method.
Enhanced AMBTC based data hiding method using hamming distance and pixel value differencing

In this paper, we propose a new Absolute Moment Block Truncation Coding (AMBTC) based data hiding scheme using hamming distance and pixel value differencing methods. The proposed method firstly pre-processes the original image using a smoothing filter so that its quality can be maintained after AMBTC compression. It then applies AMBTC compression on the processed image and uses two thresholds to categorize the image blocks into three categories namely Smooth, Less_Complex, and Highly_Complex blocks so that the complex block can also be used to embed the secret data. The secret data is then embedded into the smooth blocks using simple replacement strategy. Further, the proposed method embeds 8 bits of secret data into the bit plane of Less_Complex blocks using hamming distance calculation and few bits into the quantization levels of Highly_Complex blocks using pixel value differencing (PVD) method to increase the data hiding capacity without having any major impact on image quality. Thus, the proposed method additionally embeds on average 6 more bits in complex blocks. Experimental results also prove that the proposed method is superior to the contemporary AMBTC based data hiding methods in term of data hiding capacity and has comparable image quality.
Design and implementation of an augmented reality application for rock art visualization in Cova dels Cavalls (Spain)

Prehistoric rock art paintings, specifically rock-shelters exposed to environmental and anthropogenic factors, are usually faint and severely damaged, being them difficult to identify and understand by visitors.Augmented Reality (AR) supplements reality with virtual information superimposed onto the real world.This sensor-based technology in smartphones/tablets can improve the paintings experience displaying the 2D digital tracings overlapped onto the real scene (rock with faint paintings).This paper presents an AR application (app) developed in Cova dels Cavalls that shows a recreation of a possible original composition full of motifs with descriptive information to improve current guided tour user experiences.This case study aims to evaluate the rock art AR app targeting non-expert visitors as a means of improving rock art knowledge and sensibility of a fragile archaeological UNESCO Work Heritage site.To achieve this, a variety of participants with different backgrounds and interests tested the AR app on site and answered a complete questionnaire about the use of AR mobile apps.Overall, the results showed great acceptance of this AR app, mainly because in addition to adding new information interactively, it helps to identify the rock art motifs, as well as to recognise them quickly, improving their understanding.
Illumination-Robust remote sensing image matching based on oriented self-similarity

Establishing feature correspondences between different images is a key step in many remote sensing applications. In this paper, a novel descriptor based on an extended self-similarity measure, named HOSS (histogram of oriented self-similarity), is proposed for illumination-robust remote sensing image matching. The novel idea of HOSS is a computation of the self-similarity values in multiple directions using an oriented rectangular patch to increase the descriptor distinctiveness. For HOSS construction based on self-similarity values in various directions, a novel index map called RIMC (rotation index of the maximal correlation) incorporated with an adaptive log-polar spatial structure is proposed. Unlike conventional 2D self-similarity descriptor and its extension, HOSS is a 3D histogram of oriented self-similarity values, which is very robust to significant illumination variations. An illumination-robust rotation assignment approach is also developed based on self-similarity gradients to obtain rotation invariance. Experimental analysis on three categories of synthetic and real remote sensing images from various sensors, demonstrate the superior capability of the HOSS over state-of-the-art descriptors, including DOBSS, AB-SIFT, PIIFD, and DAISY, in terms of the recall, precision, and positional accuracy. The average performance of the HOSS in all applied images is about 36%, 51%, and 0.9 pixels in terms of recall, precision, and positional accuracy, respectively. The demo code of the HOSS approach can be downloaded from https://www.researchgate.net/publication/328733655_HOSS_Descriptor.
Siri, Echo and Performance

Don't ignore this because its about speech technology. VUIs (voice user interfaces) won a best paper in CHI 2018. Did that get your attention? Good. Siri, Ivona, Google Home, and most speech synthesis systems have voices which are based on imitating a neutral citation style of speech and making it sound natural. But, in the real world, darling, people have to act, to perform! In this paper we will talk about speech synthesis as performance, why the uncanny valley is a bankrupt concept, and how academics can escape from studying corporate speech technology as if it's been bestowed by God.
Robust Rauch–Tung–Striebel Smoothing Framework for Heavy-Tailed and/or Skew Noises

A novel robust Rauch-Tung-Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters, and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this paper is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers.
A new digital image tamper detection algorithm based on integer wavelet transform and secured by encrypted authentication sequence with 3D quantum map

In this paper, a new gray scale image authentication technique is proposed in the integer wavelet transform (IWT) domain. Digital images as an extensive media today have a special place in human life. Therefore, the importance of protecting the main concept of images and preventing the change of its original content is of particular importance. Today, a variety of methods are proposed to protect digital images that watermarking based authentication is an example of them. Some of the methods presented in this area do not have enough security at the algorithmic level, and they are usually used a common logo without encryption in embedding algorithm. This causes security weaknesses for the algorithm. So, to prevent this type of security problem for the algorithm, as well as to create a long-key encryption concept, in this paper we have used a 3D quantum map. This map has a large key space and provides long-term security for the algorithm. In addition, a new permutation algorithm is proposed and after image permutation and 2 × 2 blocking the block based Singular Value Decomposition (SVD) performs on image. The U matrix of each block is encrypted with the generated sequence by the chaos map. After generating the authentication sequence, the embedding algorithm inserts the encrypted sequence into the IWT coefficients. The simulation results of the proposed algorithm reflects the efficiency of this technique in terms of embedding visual quality, maintain the structure of the cover image, low running time and detection accuracy which have been evaluated by PSNR, SSIM, NCC, TDR, FPR and FNR metrics.
Procedural Puzzle Generation: A Survey

Procedural Content Generation (PCG) for games has existed since the 1980s and is becoming increasingly important for creating gameworlds, backstory and characters across many genres, in particular open-world games such as Minecraft (2011) and No Man's Sky (2016).A particular challenge faced by such games is that the content and/or gameplay may become repetitive.Puzzles constitute an effective technique for improving gameplay by offering players interesting problems to solve, but the use of PCG for generating puzzles has been limited compared with its use for other game elements, and efforts have focused mainly on games that are strictly puzzle games, rather than creating puzzles to be incorporated into other genres.Nevertheless, a significant body of work exists, which allows puzzles of different types to be generated algorithmically, and there is scope for much more research into this area.This paper presents a detailed survey of existing work in PCG for puzzles, reviewing 32 methods within eleven categories of puzzles.For the purpose of analysis, the paper identifies a total of seven salient characteristics related to the methods, which are used to show commonalities and differences between techniques and to chart promising areas for future research.
New Constructions of MDS Euclidean Self-Dual Codes From GRS Codes and Extended GRS Codes

In this paper, we consider the problem for which lengths a maximum distance separable (MDS) Euclidean self-dual code over Fq exists. This problem is completely solved for the case where q is even. For q is odd, some q-ary MDS Euclidean self-dual codes were obtained in the literature. In this paper, we construct six new classes of q-ary MDS Euclidean self-dual codes by using generalized Reed-Solomon (GRS for short) codes and extended GRS codes.
An HMM-based approach for automatic detection and classification of duplicate bug reports

Software projects rely on their issue tracking systems to guide maintenance activities of software developers. Bug reports submitted to the issue tracking systems carry crucial information about the nature of the crash (such as texts from users or developers and execution information about the running functions before the occurrence of a crash). Typically, big software projects receive thousands of reports every day. The aim is to reduce the time and effort required to fix bugs while improving software quality overall. Previous studies have shown that a large amount of bug reports are duplicates of previously reported ones. For example, as many as 30% of all reports in for Firefox are duplicates. While there exist a wide variety of approaches to automatically detect duplicate bug reports by natural language processing, only a few approaches have considered execution information (the so-called stack traces) inside bug reports. In this paper, we propose a novel approach that automatically detects duplicate bug reports using stack traces and Hidden Markov Models. When applying our approach to Firefox and GNOME datasets, we show that, for Firefox, the average recall for Rank k = 1 is 59%, for Rank k = 2 is 75.55%. We start reaching the 90% recall from k = 10. The Mean Average Precision (MAP) value is up to 76.5%. For GNOME, The recall at k = 1 is around 63%, while this value increases by about 10% for k = 2. The recall increases to 97% for k = 11. A MAP value of up to 73% is achieved. We show that HMM and stack traces are a powerful combination for detecting and classifying duplicate bug reports in large bug repositories.