Reviewerly/exHarmony huggingface dataset:
Simulating Homomorphic Evaluation of Deep Learning Predictions

Convolutional neural networks (CNNs) is a category of deep neural networks that are primarily used for classifying image data. Yet, their continuous gain in popularity poses important privacy concerns for the potentially sensitive data that they process. A solution to this problem is to combine CNNs with Fully Homomorphic Encryption (FHE) techniques. In this work, we study this approach by focusing on two popular FHE schemes, $$\mathsf {TFHE}$$ and $$\mathsf {HEAAN}$$ , that can work in the approximated computational model. We start by providing an analysis of the noise after each principal homomorphic operation, i.e. multiplication, linear combination, rotation and bootstrapping. Then, we provide a theoretical study on how the most important non-linear operations of a CNN (i.e. $$\max , \mathtt {Abs}, \mathtt {ReLU} $$ ), can be evaluated in each scheme. Finally, we measure via practical experiments on the plaintext the robustness of different neural networks against perturbations of their internal weights that could potentially result from the propagation of large homomorphic noise. This allows us to simulate homomorphic evaluations with large amounts of noise and to predict the effect on the classification accuracy without a real evaluation of heavy and time-consuming homomorphic operations. In addition, this approach enables us to correctly choose smaller and more efficient parameter sets for both schemes.
Transformation-Aware Similarity Measurement for Image Retargeting Quality Assessment via Bidirectional Rewarping

Image retargeting is an effective way to adapt images for target displays with different aspect ratios and sizes. Meanwhile, effective image retargeting quality assessment (IRQA) is important for optimizing the image retargeting operations. In this paper, we propose a transform-aware similarity (TRASIM) measurement metric for IRQA, including bidirectional geometric distortion measurement, bidirectional information loss measurement, and global salient structure distortion measurement. The main innovation of the TRASIM is to build a universal framework to establish the similarity transformation via bidirectional rewarping to simulate different types of retargeting operators. Based on the similarity transformation, geometric distortion and content loss are measured to determine the retargeting quality. Experimental results on two widely used databases (CUHK and RetargetMe) indicate that the proposed TRASIM has higher consistency with subjective ranks, compared with the state-of-the-art IRQA metrics.
Network Traffic Monitoring and Analysis Using Packet Sniffer

Traffic analysis using the internet is an activity to record data from user activities in using the Internet. This study aims to obtain data about the results of traffic in a graphical form so that it can find out the number of users who access the internet and use bandwidth. In this study, researchers also noted when the peak internet usage time in Telkom Vocational School Pekanbaru. The method used to get the results of the study is the packet sniffing method. Researchers can filter data packets from the http protocol application. Since, user activity is more dominant in finding and downloading sites on the Internet. The tool used is wireshark, this application is greatly helpful with features that are truly supportive and easy to analyze networks.
Generative adversarial networks model for visible watermark removal

Previously visible watermark removal algorithms required the location of known watermarks. A corresponding removal algorithm is then proposed based on the location and the feature of the watermark. If the location of the watermark is random or the watermark has different angles, the watermark removal algorithm will encounter problems. The authors recommend a visible watermark removal algorithm based on generative adversarial networks (GANs) and self-attention mechanisms. During the training, the authors introduce a GANs model to build mappings between watermarked images and real images. The authors observe that the feature of the watermarked region in different watermarked images is invariant in nature, and the other regions are changed. The self-attention layer will automatically focus on this invariant feature. Experiments on two public datasets prove that the authors’ model has gained excellent performance. Compared with the other four most competitive watermark removal models, the authors improve the watermark removal rate indicator from 17 to 92%. For the other four evaluation indicators, the authors have improved performance by up to 20%.
Automatic Generation of Problems and Explanations for an Intelligent Algebra Tutor

Intelligent tutors that emulate one-on-one tutoring with a human have been shown to effectively support student learning, but these systems are often challenging to build. Most methods for implementing tutors focus on generating intelligent explanations, rather than generating practice problems and problem progressions. In this work, we explore the possibility of using a single model of a learning domain to support the generation of both practice problems and intelligent explanations. In the domain of algebra, we show how problem generation can be supported by modeling if-then production rules in the logic programming language answer set programming. We also show how this model can be authored such that explanations can be generated directly from the rules, facilitating both worked examples and real-time feedback during independent problem-solving. We evaluate this approach through a proof-of-concept implementation and two formative user studies, showing that our generated content is of appropriate quality. We believe this approach to modeling learning domains has many exciting advantages.
Saliency Guided Image Detail Enhancement

The use of visual saliency for perceptual enhancement of images has drawn significant attention. In this paper, we explore the idea of selectively enhancing salient regions of an image. Moreover, we develop an algorithm based on adaptive bilateral filtering for this purpose. In most of the filtering based methods, detail enhancement is performed by decomposing the image into base and detail layers; the detail layer is amplified and added back to the base layer to obtain the enhanced image. The decomposition is performed using edge-preserving smoothing such as bilateral filtering. The present novelty is that we use the saliency map to locally guide the smoothing (and the enhancement) action of the bilateral filter. The effectiveness of our proposal is demonstrated using visual results. In particular, our method does not suffer from gradient reversals and halo artifacts, and does not amplify fine details in non-salient regions that often appear as noise grains in the enhanced image. Moreover, if we choose to perform the filtering channelwise, then our method can be efficiently implemented using an existing fast algorithm.
An integrated approach to the search for gold in a mature, data-rich brownfields environment: A case study from Sigma-Lamaque, Quebec

The greater than 1300 participants in the 2015 Integra Gold Rush Challenge, the biggest gold exploration crowdsourcing competition to date, were given access to six terabytes of historic data collected at the c. 12 Moz Au Sigma-Lamaque gold deposits, Val-d'Or district, Quebec, and asked to identify targets with the highest potential for the next big gold discovery at the property. This paper describes our approach to exploration targeting at Sigma-Lamaque with particular emphasis on (i) integrating and interrogating the available data; (ii) identifying mappable expressions/key ingredients of ore-forming processes using spatial data modelling (in particular inversion modelling and advanced filtering of the available geophysical data); (iii) developing a conceptual model in the framework of a mineral systems approach; and (iv) translating the conceptual model into an effective tool for targeting gold in the Val-d'Or district.
Fast and Accurate Entity Linking via Graph Embedding

Entity Linking, the task of mapping ambiguous Named Entities to unique identifiers in a knowledge base, is a cornerstone of multiple Information Retrieval and Text Analysis systems. So far, no single entity linking algorithm has been able to offer the accuracy and scalability required to deal with the ever-increasing amount of data in the web and become a de-facto standard.
Virtually the Same Experience?

In order to investigate how a VR study context influence participants' User Experience responses of an interactive system an UX evaluation of the same in-vehicle systems was conducted in the field and in virtual reality. The virtual environment featured a virtual road scene and an interactive in-car environment, paired with a physical set-up containing a table-mounted steering wheel and a touch-sensitive panel. The VR system enabled a high estimation of presence and focus, however participants voiced less affect in the virtual setting and had difficulty in separating judgments of the VR experience from the UX of the in-vehicle systems. No significant differences in UX questionnaire data were identified between VR and the field, but there were correlations between rated presence in the VR system and UX ratings, especially for reported stimulation. Based on the lessons learned, a number of methodological and technological consequences are recommended in the paper, such as the need for more dynamic movement behaviour, improved resolution of graphics of the virtual vehicle and introducing the test leader as visually present in the virtual environment.
Can Cryptocurrencies Preserve Privacy and Comply With Regulations?

Cryptocurrencies offer an alternative to traditional methods of electronic value exchange, promising anonymous, cash-like electronic transfers, but in practice they fall short for several key reasons. We consider the false choice between total surveillance, as represented by banking as currently implemented by institutions, and impenetrable lawlessness, as represented by privacy-enhancing cryptocurrencies as currently deployed. We identify a range of alternatives between those two extremes, and we consider two potential compromise approaches that offer both the auditability required for regulators and the anonymity required for users.
A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables

Benign normal (NL), premalignant (endometrial intraepithelial neoplasia, EIN) and malignant (cancer, EMCA) endometria must be precisely distinguished for optimal management. EIN was objectively defined previously as a regression model incorporating manually traced histologic variables to predict clonal growth and cancer outcomes. Results from this early computational study were used to revise subjective endometrial precancer diagnostic criteria currently in use. We here use automated feature segmentation and updated machine learning algorithms to develop a new classification algorithm. Endometrial tissue from 148 patients was randomly separated into 72-patient training and 76-patient validation cohorts encompassing all 3 diagnostic classes. We applied image analysis software to keratin stained endometrial tissues to automatically segment whole-slide digital images into epithelium, cells, and nuclei and extract corresponding variables. A total of 1413 variables were culled to 75 based on random forest classification performance in a 3-group (NL, EIN, EMCA) model. This algorithm correctly classifies cases with 3-class error rates of 0.04 (training set) and 0.058 (validation set); and 2-class (NL vs. EIN+EMCA) error rate of 0.016 (training set) and 0 (validation set). The 4 most heavily weighted variables are surrogates of those previously identified in manual-segmentation machine learning studies (stromal and epithelial area percentages, and normalized epithelial surface lengths). Lesser weighted predictors include gland and lumen axis lengths and ratios, and individual cell measures. Automated image analysis and random forest classification algorithms can classify normal, premalignant, and malignant endometrial tissues. Highest predictive variables overlap with those discovered independently in early models based on manual segmentation.
Sign Language Recognition System Using Deep Neural Network

In the current fast-moving world, human-computer- interactions (HCI) is one of the main contributors towards the progress of the country. Since the conventional input devices limit the naturalness and speed of human-computer- interactions, Sign Language recognition system has gained a lot of importance. Different sign languages can be used to express intentions and intonations or for controlling devices such as home robots. The main focus of this work is to create a vision based system, a Convolutional Neural Network (CNN) model, to identify six different sign languages from the images captured. The two CNN models developed have different type of optimizers, the Stochastic Gradient Descent (SGD) and Adam.
AtSNE

Visualization of high-dimensional data is a fundamental yet challenging problem in data mining. These visualization techniques are commonly used to reveal the patterns in the high-dimensional data, such as clusters and the similarity among clusters. Recently, some successful visualization tools (e.g., BH-t-SNE and LargeVis) have been developed. However, there are two limitations with them : (1) they cannot capture the global data structure well. Thus, their visualization results are sensitive to initialization, which may cause confusions to the data analysis. (2) They cannot scale to large-scale datasets. They are not suitable to be implemented on the GPU platform because their complex algorithm logic, high memory cost, and random memory access mode will lead to low hardware utilization. To address the aforementioned problems, we propose a novel visualization approach named as Anchor-t-SNE (AtSNE), which provides efficient GPU-based visualization solution for large-scale and high-dimensional data. Specifically, we generate a number of anchor points from the original data and regard them as the skeleton of the layout, which holds the global structure information. We propose a hierarchical optimization approach to optimize the positions of the anchor points and ordinary data points in the layout simultaneously. Our approach presents much better and robust visual effects on 11 public datasets, and achieve 5 to 28 times speed-up on different datasets, compared with the current state-of-the-art methods. In particular, we deliver a high-quality 2-D layout for a 20 million and 96-dimension dataset within 5 hours, while the current methods fail to give results due to running out of the memory.
Realizing quantum linear regression with auxiliary qumodes

In order to exploit quantum advantages, quantum algorithms are indispensable for operating machine learning with quantum computers. We here propose an intriguing hybrid approach of quantum information processing for quantum linear regression, which utilizes both discrete and continuous quantum variables, in contrast to existing wisdoms based solely upon discrete qubits. In our framework, data information is encoded in a qubit system, while information processing is tackled using auxiliary continuous qumodes via qubit-qumode interactions. Moreover, it is also elaborated that finite squeezing is quite helpful for efficiently running the quantum algorithms in a realistic setup. Comparing with an all-qubit approach, the present hybrid approach is more efficient and feasible for implementing quantum algorithms, still retaining exponential quantum speed-up.
Efficient Architectures for Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes allow localized decoding of short sub-codewords. They are essential to hyper-speed data storage, communications, and continued scaling of distributed storage. Sub-codewords, which are usually Reed–Solomon (RS) or BCH codewords, are nested to generate codewords of higher correction capabilities. If the decoding of individual sub-codewords fails, the higher-order syndromes from the nested codewords are utilized to correct more errors. GII decoder design faces many challenges. The major ones include: 1) high-speed nested decoding utilizing the higher-order syndromes; 2) efficient updating of higher-order syndromes after sub-codewords are corrected; 3) computation of nested syndromes and matrix inversion for converting them to higher-order sub-codeword syndromes; and 4) efficient architectures capable of addressing the variable correction capabilities of the nested codewords. This paper proposes novel algorithmic reformulations and architectural transformations to address each bottleneck. For an example, GII code that has the same rate and length as eight un-nested (255, 223) RS codes, the proposed GII decoder achieves more than seven orders of magnitude improvement in error-correcting performance with less than 30% area overhead compared to the RS decoder. With a critical path of seven XOR gates, the proposed decoder can easily achieve more than 40 GB/s throughput.
Deep Constrained Low-Rank Subspace Learning for Multi-View Semi-Supervised Classification

Semi-supervised classification receives increasing interests because it can predict class labels based on both limited labeled and sufficient unlabeled data. In this letter, we propose a deep constrained low-rank subspace learning (DCLSL) method for multi-view semi-supervised classification. Specifically, we integrate deep constrained matrix factorization, low-rank subspace learning, and class label learning into a unified objective function to jointly learn data similarity matrices and class label matrix. DCLSL is able to obtain the discriminative subspace representation of each view and effectively aggregate similarity matrices of multiple views, resulting in better classification performance. Experimental results on various datasets demonstrate the effectiveness of our method.
Design of a chaos-based encryption scheme for sensor data using a novel logarithmic chaotic map

This paper introduces a novel Logarithmic Chaotic Map (LCM) that is inspired by the well known quadratic map. The chaotic dynamic of the proposed map LCM is thoroughly investigated and its chaotic properties are found to be superior in comparison with the existing maps in the literature. In addition, a new encryption algorithm is proposed that is based on LCM. The chaotic output generated from LCM passed through all 15 National Institute of Standards and Technology (NIST) statistical tests thus confirming its randomness. The proposed encryption algorithm is best suited for encrypting numeric sensor data values preferably for IoT applications and has been found to be highly resilient to security attacks.
The Secret Lives of Names?

Your name tells a lot about you: your gender, ethnicity and so on. It has been shown that name embeddings are more effective in representing names than traditional substring features. However, our previous name embedding model is trained on private email data and are not publicly accessible. In this paper, we explore learning name embeddings from public Twitter data. We argue that Twitter embeddings have two key advantages: (i) they can and will be publicly released to support research community. (ii) even with a smaller training corpus, Twitter embeddings achieve similar performances on multiple tasks comparing to email embeddings.
Data Rhetoric and Uneasy Alliances

To gain historical perspective on the role of technical expertise in the labor movement, we explore the data-driven practices of mid-century American labor unionists who appropriated techniques from scientific management to advocate for workers. Analyzing the data artifacts and academic writings of the Management Engineering department of the International Ladies Garment Workers Union, we describe the rhetorical use of data within a mutual-gains model of participation. We draw insights from the challenges faced by the department, assess the feasibility of implementing these approaches in the present, and identify opportunities for the participatory design of workplace advocacy systems moving forward.
Salient Features for Moving Object Detection in Adverse Weather Conditions During Night Time

Foreground segmentation of moving objects in adverse atmospheric conditions such as fog, rain, low light, and dust is a challenging task in computer vision. The advantages of thermal infrared imaging at night time under adverse atmospheric conditions have been demonstrated, which are due to the long wavelength. However, the existing state-of-the-art object detection techniques have not been useful in such scenarios. In this paper, we propose an improved background model that utilizes both thermal pixel intensity features and spatial video salient features. The proposed spatial video salient features are represented as an Akin-based per-pixel Boolean string over a local region block, and depend on the effect of neighboring pixels on a center pixel. The result of this Boolean procedure is referred to as the- Akin-Based Local Whitening Boolean Pattern (ALWBP), which differentiates foreground and background region accurately, even against a cluttered background. The background model is controlled via 1) the automatic adaptation of parameters such as the decision threshold $\text{R}_{\mathrm {T}}$ and, learning parameter L, and 2) the updating of background samples $\text{B}_{\mathrm {sample\_{}int}}$ and,- $\text{B}_{\mathrm {sample\_{}ALWBP}}$ to minimize 1) the effect of the background dynamics of outdoor scenes and 2) the temperature polarity changes during the maiden appearance of a moving object in thermal frame sequences. The performance of this model is evaluated using nine existing standard segmentation performance metrics on our newly created-Tripura University Video Dataset at Night Time (TU-VDN) and on the publicly available CDnet-2014 dataset. Our newly created weather-degraded video dataset, namely, TU-VDN, consists of sixty video sequences that represent four atmospheric conditions, namely, low light, dust, rain, and fog. The results of a performance comparison with 14 state-of-the-art detection techniques also demonstrate the high accuracy of the proposed technique.
A Survey of Deep Learning Solutions for Multimedia Visual Content Analysis

The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos.Such media is full of visual information and holds immense value in today's world.The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness.Deep learning (DL) has recently emerged as a prominent technique for visual content analysis.It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors.Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints.This paper identifies eight problem domains which require analysis of visual artifacts in multimedia.It surveys the recent, authoritative, and the best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems.This paper also discusses the challenges that the DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.