Reviewerly/exHarmony huggingface dataset:
Project and Forget: Solving Large Scale Metric Constrained Problems

Given a set of dissimilarity measurements amongst data points, determining what metric representation is most consistent with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms. Existing methods are restricted to specific kinds of metrics or small problem sizes because of the large number of metric constraints in such problems. In this paper, we provide an active set algorithm, Project and Forget, that uses Bregman projections, to solve metric constrained problems with many (possibly exponentially) inequality constraints. We provide a theoretical analysis of \textsc{Project and Forget} and prove that our algorithm converges to the global optimal solution and that the $L_2$ distance of the current iterate to the optimal solution decays asymptotically at an exponential rate. We demonstrate that using our method we can solve large problem instances of three types of metric constrained problems: general weight correlation clustering, metric nearness, and metric learning; in each case, out-performing the state of the art methods with respect to CPU times and problem sizes.
An image encryption method based on logistic chaotic mapping and DNA coding

This paper proposes a new image encryption algorithm, which is based on Logistic chaotic mapping and DNA coding. A variable parameter logistic mapping coupled chaotic system is proposed to generate pseudo-random sequences with good pseudo-random properties. Before encrypting plain-text images, the distinct features of some special images are eliminated by superposing them with a pseudo-random image. Combining with DNA encoding operation, an encrypting algorithm of cross-operation of diffusion and permutation is designed. The simulation results show that the proposed algorithm has large key space, high sensitivity to key and plain-text, and good robustness to resist exhaustive attack, statistic attack, differential attack, etc. It shows that the proposed method has good encryption effect and security.
Color Image Enhancement by Using Hue-Saturation Gradient

It is necessary to preserve the hue component of color image in color image processing. Although the hue component can not be processed, it can be used for color image processing. However, the hue component has not been used for color image processing until now. It is already known that saturation components work effectively in color image enhancement. Therefore, in this paper, we define the hue-saturation gradient which is a gradient combining the saturation gradient and the hue gradient. The differential hue-saturation histogram equalization with variable enhancement degree (DHSHEwVED) is proposed. The proposed method can emphasize both intensity and saturation. The proposed method has two parameters, which can be used to adjust the contrast and colorfulness independently. We shows the effectiveness of proposed method from image processing results.
LiDAR Data Integrity Verification for Autonomous Vehicle Using 3D Data Hiding

Autonomous vehicle is becoming a complex cyber-physical system with many interfaces to the external world like Wi-Fi, Bluetooth, cellular, and vehicle to anything (v2x) networks. These interfaces open new attack surfaces that can put the onboard sensors used in autonomous driving at risk of internal and external cyber-attacks that are capable of manipulating the sensor data. Since the control algorithms that define the autonomous vehicle behavior rely on the data from these onboard sensors like LiDAR, camera and RADAR, failure to secure the sensor data could lead to erroneous decisions and may result in fatal accidents. In this paper, we propose a 3D QIM based data- hiding technique to secure the raw data from LiDAR sensor. The proposed technique detects the tampering of the LiDAR sensor data and also locates the tampered region. The evaluation of the proposed method on KITTI dataset showed that the method can successfully detect and localize insider data tampering attacks such as fake target insertion (FTI) and valid target deletion (VTD).
StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization.

Traditional preneural approaches to single document summarization relied on modeling the intermediate structure of a document before generating the summary. In contrast, the current state of the art neural summarization models do not preserve any intermediate structure, resorting to encoding the document as a sequence of tokens. The goal of this work is two-fold: to improve the quality of generated summaries and to learn interpretable document representations for summarization. To this end, we propose incorporating latent and explicit sentence dependencies into single-document summarization models. We use structure-aware encoders to induce latent sentence relations, and inject explicit coreferring mention graph across sentences to incorporate explicit structure. On the CNN/DM dataset, our model outperforms standard baselines and provides intermediate latent structures for analysis. We present an extensive analysis of our summaries and show that modeling document structure reduces copying long sequences and incorporates richer content from the source document while maintaining comparable summary lengths and an increased degree of abstraction.
Bimodal Emotion Recognition Using Audio and Facial Features

A multimodal emotion recognition system is proposed using speech and facial images. For this purpose a video database is developed, containing emotions in three affective states viz. anger, sad and happiness. The audio and the snapshots of facial expressions acquired from the videos constituted the bimodal input for recognizing emotions. The spoken sentences in the database included text dependent as well as text independent sentences in Malayalam language. The audio features included short-time processing of speech to obtain: energy, zero crossing count, pitch and Mel Frequency Cepstral Coefficients. For facial expressions, the landmark features of face: eyebrows, eyes and mouth, obtained using Viola Jones Algorithm is used. The supervised learning methods K-Nearest Neighbor and Artificial Neural Network are used for emotion analysis. The system performance is evaluated for 3 cases viz. using audio based features and facial features separately and for both features taken together. Further, the effect of text dependent and text independent audio is also analyzed. The result of the analysis shows that text independent videos (utilizing both modalities) using K -Nearest Neighbor (highest accuracy 82.78%) is found to be more effective in recognizing emotions from the database considered.
SelfSphNet: Motion Estimation of a Spherical Camera via Self-Supervised Learning

In this paper, we propose SelfSphNet, that is, a self-supervised learning network to estimate the motion of an arbitrarily moving spherical camera without the need for any labeled training data. Recently, numerous learning-based methods for camera motion estimation have been proposed. However, most of these methods require an enormous amount of labeled training data, which is difficult to acquire experimentally. To solve this problem, our SelfSphNet employs two loss functions to estimate the frame-to-frame camera motion, thus giving two supervision signals to the network with the usage of unlabeled training data. First, a 5 DoF epipolar angular loss, which is composed of a dense optical flow of spherical images, estimates the 5 DoF motion between two image frames. This loss function utilizes a unique property of the spherical optical flow, which allows the rotational and translational components to be decoupled by using a derotation operation. This operation is derived from the fact that spherical images can be rotated to any orientation without any loss of information, hence making it possible to “decouple” the dense optical flow between pairs of spherical images to a pure translational state. Next, a photometric reprojection loss estimates the full 6 DoF motion using a depth map generated from the decoupled optical flow. This minimization strategy enables our network to be optimized without using any labeled training data. To confirm the effectiveness of our proposed approach (SelfSphNet), several experiments to estimate the camera trajectory, as well as the camera motion, were conducted in comparison to a previous self-supervised learning approach, SfMLearner, and a fully supervised learning approach whose baseline network is the same as SelfSphNet. Moreover, transfer learning in a new scene was also conducted to verify that our proposed method can optimize the network with newly collected unlabeled data.
Entertainment and Enjoyment as Media Effect

The scientific study of media entertainment has grown considerably in the quarter century since the first edition of this volume was published. This chapter summarizes and synthesizes research conducted during that time. First, motivations for selecting media entertainment are discussed, with specific emphasis on persistent and situational need satisfaction, as well as individual-difference factors. Second, processes involved in the enjoyment of entertainment (i.e., entertainment theory) are examined, including the role of characters (e.g., affective dispositions, identification), content features, reception processes (e.g., narrative engagement, social comparisons), and receptions contexts. The chapter concludes with thoughts on what work remains to be done.
Depicting More Information in Enriched Squarified Treemaps with Layered Glyphs

The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as the few options for visual data mappings and the inability to represent zero and negative values. Additionally, visualizing high dimensional data requires many hierarchies, which can impair data visualization. Thus, this paper proposes to add layered glyphs to Treemap’s items to mitigate these issues. Layered glyphs are composed of N partially visible layers, and each layer maps one data dimension to a visual variable. Since the area of the upper layers is always smaller than the bottom ones, the layers can be stacked to compose a multidimensional glyph. To validate this proposal, we conducted a user study to compare three scenarios of visual data mappings for Treemaps: only Glyphs (G), Glyphs and Hierarchy (GH), and only Hierarchy (H). Thirty-six volunteers with a background in InfoVis techniques, organized into three groups of twelve (one group per scenario), performed 8 InfoVis tasks using only one of the proposed scenarios. The results point that scenario GH presented the best accuracy while having a task-solving time similar to scenario H, which suggests that representing more data in Treemaps with layered glyphs enriched the Treemap visualization capabilities without impairing the data readability.
SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks.

We introduce SketchGCN, a graph convolutional neural network for semantic segmentation and labeling of free-hand sketches. We treat an input sketch as a 2D pointset, and encode the stroke structure information into graph node/edge representations. To predict the per-point labels, our SketchGCN uses graph convolution and a global-local branching network architecture to extract both intra-stroke and inter-stroke features. SketchGCN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.4% in the pixel-basedmetric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.
Dummy Generation-Based Privacy Preservation for Location-Based Services

Location-Based Services (LBS) are a type of applications, which requires user's location information as input in providing useful services [4]. Google Maps (Online Navigation Applications), Foursquare (Geo-Social Network), Strava (Fitness Applications), etc. are some of the examples of LBS applications. For all the LBS applications, privacy is a major concern. LBS user may not be interested in revealing its where-about information to anyone, not even to LBS provider.
Pricing ASICs for Cryptocurrency Mining

Cryptocurrencies that are based on Proof-of-Work often rely on special purpose hardware (ASICs) to perform mining operations that secure the system. We argue that ASICs have been mispriced by miners and sellers that only consider their expected returns, and that in fact mining hardware should be treated as a bundle of \emph{financial options}, that when exercised, convert electricity to virtual coins. We provide a method of pricing ASICs based on this insight, and compare the prices we derive to actual market prices. Contrary to the widespread belief that ASICs are worth less if the cryptocurrency is highly volatile, we show the opposite effect: volatility significantly increases value. Thus, if a coin's volatility decreases, some miners may leave, affecting security. To prevent this, we suggest a new reward mechanism. Finally we construct a portfolio of coins and bonds that provides returns imitating an ASIC, and evaluate its behavior: historically, realized revenues of such portfolios have significantly outperformed ASICs, showing that indeed there is a mispricing of hardware, and offering an alternative investment route for would-be miners.
Algorithms Acting Badly: A Solution from Corporate Law

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Improving Feature Attribution through Input-specific Network Pruning

Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks. Due to the complexity of current network architectures, current gradient-based attribution methods provide very noisy or coarse results. We propose to prune a neural network for a given single input to keep only neurons that highly contribute to the prediction. We show that by input-specific pruning, network gradients change from reflecting local (noisy) importance information to global importance. Our proposed method is efficient and generates fine-grained attribution maps. We further provide a theoretical justification of the pruning approach relating it to perturbations and validate it through a novel experimental setup. Our method is evaluated by multiple benchmarks: sanity checks, pixel perturbation, and Remove-and-Retrain (ROAR). These benchmarks evaluate the method from different perspectives and our method performs better than other methods across all evaluations.
An improved weighted fusion algorithm of multi-sensor

Abstract Multi-sensor data fusion is to take full advantage of the complementary nature of multivariate data to improve the feasibility of the statistics. The weighted fusion algorithm is commonly used due to its easiness to achieve. Among the relative factors, the weight directly impacts the results of the data fusion, therefore, the selection of weight is particularly important, as choosing an inappropriate weight will lead to the instability of algorithm performance. To find the best weight, we develop an improved weighted fusion algorithm, introducing the concept of the optimal proportion weight and using secondary weighted approach ----- the single sensor will be weighted individually before the whole sensor system is weighted in order to achieve the optimal algorithm performance.
{XRD}: Scalable Messaging System with Cryptographic Privacy

Even as end-to-end encrypted communication becomes more popular, private messaging remains a challenging problem due to metadata leakages, such as who is communicating with whom. Most existing systems that hide communication metadata either (1) do not scale easily, (2) incur significant overheads, or (3) provide weaker guarantees than cryptographic privacy, such as differential privacy or heuristic privacy. This paper presents XRD (short for Crossroads), a metadata private messaging system that provides cryptographic privacy, while scaling easily to support more users by adding more servers. At a high level, XRD uses multiple mix networks in parallel with several techniques, including a novel technique we call aggregate hybrid shuffle. As a result, XRD can support 2 million users with 251 seconds of latency with 100 servers. This is 12x and 3.7x faster than Atom and Pung, respectively, which are prior scalable messaging systems with cryptographic privacy.
The Impact of Constant Field of Attention on Properties of Contextual Neural Networks

Applications of Artificial Neural Networks are used with success in many fields such as e.g. medicine, economy, science and entertainment. But most of those models processes all input signals without attention directing mechanisms. In this paper contextual neural networks are considered which are using multi-step conditional aggregation functions to direct attention of neurons during data processing. It is verified if aggregation function with constant field of attention (CFA) can help to build classification models of higher accuracy and of lower activity of hidden connections than aggregation function with dynamic field of attention (SIF). Experiments are performed with use of the H2O machine learning framework implementing Generalized Backpropagation Algorithm for selected benchmark problems from UCI ML repository and real-life ALL-AML leukemia gene expression data. Presented analysis of results gives important clues on the most promising directions of research on contextual neural networks and indicates possible improvements of the H2O framework.
Research on the Change of Educational Management in the Era of Artificial Intelligence

With the development and progress of artificial intelligence, artificial intelligence has become a hot topic of discussion in the current society. At present, people from all walks of life have different opinions on artificial intelligence technology. However, it is worth noting that in the future, artificial intelligence technology will inevitably enter all walks of life and greatly improve people's quality of life. In addition, artificial intelligence simulates human intelligence, which is in line with the education industry. With the continuous development of artificial intelligence, it has gradually combined with educational management, which makes educational management more forward-looking, and gradually promotes the development of educational management towards the direction of data, transparency and rationalization. However, with the gradual integration of artificial intelligence and education management, a series of problems have arisen, such as violation of privacy and security, violation of ethics and so on, which have brought enormous challenges to education management. In other words, there are advantages and disadvantages in integrating artificial intelligence technology into education management. Therefore, the author believes that education managers should adopt scientific and efficient measures, such as clearing up the working boundary of human and artificial intelligence in educational management activities, improving the relevant legal system, strengthening the relevant legal functions, and educating subjects should do their best and make appropriate use of artificial intelligence technology. Only in this way can we give full play to the respective advantages of artificial intelligence technology and education management, and further promote the development of education management towards modernization and intellectualization.
All-optical recurrent neural network with sigmoid activation function

We demonstrate experimentally, the first all-optical recurrent-neuron with a sigmoid activation function and four WDM-inputs with 100psec pulses. The proposed neuron geared up a neural-network for financial prediction-tasks exhibiting an accuracy of 42.57% on FI-2010.
Acceleration of Genetic Algorithm on GPU CUDA Platform

When a deterministic search approach is too costly, such as for non-deterministic polynomial-hard problems, finding near-optimal solutions with approximation algorithms, such as the genetic algorithm, is the only practical approach to reduce the execution time. In this paper, we exploit the capability of graphics processing units (GPU), specifically Nvidia's CUDA platform, to accelerate the genetic algorithm by modifying the evolutionary operations to fit the hardware architecture. This has allowed us to achieve significant computational speedups compared to the non-GPU counterparts.
Virtual Laboratories: Engineers’ Training for Automotive Industry

Transition to Industry 4.0 is accompanied by the rapid development of engineering and technologies that results in a paradigm shift in the processes organization. Thus, there is a n necessity to improve the competencies of workers, in particular, engineers for high-tech industries. Therefore, there is a need for educational technologies development, especially, in the areas of virtual and augmented reality. Article introduces an overview of the problem status, provides positive examples of creating a virtual reality laboratory, as well as advantages with respect to traditional training methods. Authors describe a case study of virtual reality based methodology implementation to organize laboratory classes for the training of automechanics engineers. Article presents the comparison between the traditional training system and the innovative one based on virtual reality. The main advantages of this innovative virtual reality based system's application are given. In order to assess these methods, indicators have been introduced and have been implemented into the examination done among 2 groups of students: 1 - using traditional training methods and the other - using virtual reality based ones.
Sequential Time-Optimal Algorithm for Extended Path Tracking Problem

Abstract This paper presents a fast and easily implementable path tracking algorithm for robots. Usually, for a path tracking problem, the goal is to move the robot on a predefined path, while the joint velocities and accelerations are kept within their limits. This paper deals with the extended case, constraining the forces applied to the objects at the manipulator. First, a problem with a special set of constraints is presented, and a sequential solver method is formulated. The presented sequential solver algorithm has significant computational benefits compared to the direct transcription approach. Then, a practical example is introduced where the proposed algorithm can be applied. At last, the algorithm is validated by real-life experimental results with a six degrees-of-freedom robotic arm.