Reviewerly/exHarmony huggingface dataset:
Cold Start in Recommender Systems—A Survey from Domain Perspective

In recent years, the growth of the Internet has led to the emergence of recommender systems, as embedded engines, in various domains. These engines are in a way helping tools for users, as they suggest items and services according to users’ tastes and preferences. But these recommendations depend upon past user/item liking history. Non-availability of past user/item history leads to the situation of cold start. The cold-start problem is one of the major factors affecting the efficiency of recommender systems. This survey explores the cold-start problem from a domain perspective. The scenarios of cold start in different domains and techniques used to find solutions have been explored. Most of the researches have proposed solutions for e-commerce/e-business sites as they have more economic value. In recent years, deep learning techniques have gained momentum and are being used for solving cold start. Solutions for this problem in domains other than e-commerce/e-business need to be explored using deep learning methods.
Formation of Civil Responsibility in Youth the Basis of a Stable and Prosperous Life in Uzbekistan

The article analyzes the socio-philosophical importance of the formation of civil responsibility in young people in the process of building a democratic legal state and civil society in Uzbekistan. Attention is also paid to the issue of factors affecting the formation of civil responsibility in young people. The article also examined the issues of increasing the legal literacy of citizens, comprehensive development of medium and small entrepreneurship, formation and strengthening of a sense of responsibility in young people, national self-awareness, restoration of historical memory, raising national pride and pride, strengthening ideological immunity.
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms

We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem. This semi-infinite dual admits a finite dimensional representation, but its support is over a convex set which is difficult to characterize. In particular, we demonstrate that the non-convex neural network training problem is equivalent to a finite-dimensional convex copositive program. Our work is the first to identify this strong connection between the global optima of neural networks and those of copositive programs. We thus demonstrate how neural networks implicitly attempt to solve copositive programs via semi-nonnegative matrix factorization, and draw key insights from this formulation. We describe the first algorithms for provably finding the global minimum of the vector output neural network training problem, which are polynomial in the number of samples for a fixed data rank, yet exponential in the dimension. However, in the case of convolutional architectures, the computational complexity is exponential in only the filter size and polynomial in all other parameters. We describe the circumstances in which we can find the global optimum of this neural network training problem exactly with soft-thresholded SVD, and provide a copositive relaxation which is guaranteed to be exact for certain classes of problems, and which corresponds with the solution of Stochastic Gradient Descent in practice.
Resource allocation and scheduling in the intelligent edge computing context

With the rapid development of the edge computing, Internet economy has become a new situation in economic development. What has brought development to the Internet economy is a variety of family-based platforms (i.e., edge computing). However, in the face of huge Internet users and different users’ shopping habits, reasonably personalized e-commerce platform allocations for users can improve the user’s website shopping experience. However, due to the huge user data and the diversity of SDN (software defined network) platforms, it is a huge challenge to reasonably allocate e-commerce resources/platforms to users. The quality of the SDN personalized resource allocations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can allocate the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized allocation faces the problem of too high user data dimensions. Therefore, we first conduct computational efficiency analysis toward each user. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that, in the edge computing context, our method can reduce the complexity of resource allocation toward complex data and improve the quality of the allocated data.
Research on path planning method of forging handling robot based on combined strategy

In the robot navigation module, autonomous path planning is at the core, and the path planning control strategy is the key factor. Traditional robot path planning algorithms generally have some inherent defects, and they cannot seamlessly integrate local and global path planning methods, resulting in low efficiency. This paper proposes a combination strategy of local and global path planning algorithms. In the global algorithm, the potential field ant colony algorithm is adopted. Before the robot starts, it will plan a relatively optimal path on the specified electronic map, which solves the problems of premature convergence and low efficiency of the traditional ant colony algorithm; in terms of local algorithms The improved artificial potential field method is adopted, which carries out real-time path planning for the unknown environment, and solves the problems of local minima and unreachable targets in the traditional potential field method. In this paper, the proposed improved method is simulated and analyzed, and the results show that the proposed control method is feasible.
Data integrity auditing for secure cloud storage using user behavior prediction

Data integrity is a core security issue in reliable cloud storage that has received widespread attention. Data auditing protocols enable verifiers to efficiently check the integrity of outsourced data without downloading the data. A key research challenge associated with the design of existing data auditing protocols is the efficiency of the auditing process. Since existing protocols tend to auditing all cloud data, in fact, some data may have just been used or will be used soon, then auditing all these data is invalid, which is a waste of resources. In this paper, we attempt to address the waste of resources due to invalid auditing in cloud data integrity checking by introducing user behavior prediction algorithms,the first in such an approach, to the best of our knowledge. More specifically, we introduce the concept of valid auditing in data integrity verification based on the concept of valid auditing. We formalize a system model and a security model for this new concept. Then, using user behavior prediction algorithms, we give methods to improve auditing efficiency and reduce invalid auditing. Finally, we develop a prototype implementation of the protocol to validate the practicality of the scheme.
Authentic Boundary Proximal Policy Optimization

In recent years, the proximal policy optimization (PPO) algorithm has received considerable attention because of its excellent performance in many challenging tasks. However, there is still a large space for theoretical explanation of the mechanism of PPO’s horizontal clipping operation, which is a key means to improve the performance of PPO. In addition, while PPO is inspired by the learning theory of trust region policy optimization (TRPO), the theoretical connection between PPO’s clipping operation and TRPO’s trust region constraint has not been well studied. In this article, we first analyze the effect of PPO’s clipping operation on the objective function of conservative policy iteration, and strictly give the theoretical relationship between PPO and TRPO. Then, a novel first-order policy gradient algorithm called authentic boundary PPO (ABPPO) is proposed, which is based on the authentic boundary setting rule. To ensure the difference between the new and old policies is better kept within the clipping range, by borrowing the idea of ABPPO, we proposed two novel improved PPO algorithms called rollback mechanism-based ABPPO (RMABPPO) and penalized point policy difference-based ABPPO (P3DABPPO), which are based on the ideas of rollback clipping and penalized point policy difference, respectively. Experiments on the continuous robotic control tasks implemented in MuJoCo show that our proposed improved PPO algorithms can effectively improve the learning stability and accelerate the learning speed compared with the original PPO.
Analysis of Content Curation Algorithms on Personalized Web Searching

We are going towards the Web world and IoT with the speed of light. Personalization and Recommendation systems are the pillars of web world. Now a day’s users want to search the relevant content on web in minimum amount of time. It’s hard to make an efficient system which searches the relevant data to the user on a click. To show the curated results and recommends the best search results as per user’s interest is a typical task, and gets more complicated when we are going to apply on web data. In this paper, an approach and proposed model is discussed which is based on user’s personal interest and automatically recommends the relevant information as per his/her interest. Hybrid computing is used to make the algorithm and process the data to refine the results. Some existing personalized searching techniques and models are also compared as literature survey.
On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation

We investigate the sensitivity of the Fr\'echet Inception Distance (FID) score to inconsistent and often incorrect implementations across different image processing libraries. FID score is widely used to evaluate generative models, but each FID implementation uses a different low-level image processing process. Image resizing functions in commonly-used deep learning libraries often introduce aliasing artifacts. We observe that numerous subtle choices need to be made for FID calculation and a lack of consistencies in these choices can lead to vastly different FID scores. In particular, we show that the following choices are significant: (1) selecting what image resizing library to use, (2) choosing what interpolation kernel to use, (3) what encoding to use when representing images. We additionally outline numerous common pitfalls that should be avoided and provide recommendations for computing the FID score accurately. We provide an easy-to-use optimized implementation of our proposed recommendations in the accompanying code.
Gradient Methods Never Overfit On Separable Data

A line of recent works established that when training linear predictors over separable data, using gradient methods and exponentially-tailed losses, the predictors asymptotically converge in direction to the max-margin predictor. As a consequence, the predictors asymptotically do not overfit. However, this does not address the question of whether overfitting might occur non-asymptotically, after some bounded number of iterations. In this paper, we formally show that standard gradient methods (in particular, gradient flow, gradient descent and stochastic gradient descent) never overfit on separable data: If we run these methods for $T$ iterations on a dataset of size $m$, both the empirical risk and the generalization error decrease at an essentially optimal rate of $\tilde{\mathcal{O}}(1/\gamma^2 T)$ up till $T\approx m$, at which point the generalization error remains fixed at an essentially optimal level of $\tilde{\mathcal{O}}(1/\gamma^2 m)$ regardless of how large $T$ is. Along the way, we present non-asymptotic bounds on the number of margin violations over the dataset, and prove their tightness.
Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

Biometric security applications have been employed for providing a higher security in several access control systems during the past few years. The handwritten signature is the most widely accepted behavioral biometric trait for authenticating the documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life. In this paper, a novel algorithm to detect gender of individuals based on the image of their handwritten signatures is proposed. The proposed work is based on the fusion of textural and statistical features extracted from the signature images. The LBP and HOG features represent the texture. The writer’s gender classification is carried out using machine learning techniques. The proposed technique is evaluated on own dataset of 4,790 signatures and realized an encouraging accuracy of 96.17, 98.72 and 100% for k-NN, decision tree and Support Vector Machine classifiers, respectively. The proposed method is expected to be useful in design of efficient computer vision tools for authentication and forensic investigation of documents with handwritten signatures.
Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors

Single-view 3D object reconstruction has seen much progress, yet methods still struggle generalizing to novel shapes unseen during training. Common approaches pre- dominantly rely on learned global shape priors and, hence, disregard detailed local observations. In this work, we address this issue by learning a hierarchy of priors at different levels of locality from ground truth input depth maps. We argue that exploiting local priors allows our method to efficiently use input observations, thus improving generalization in visible areas of novel shapes. At the same time, the combination of local and global priors enables meaningful hallucination of unobserved parts resulting in consistent 3D shapes. We show that the hierarchical approach generalizes much better than the global approach. It generalizes not only between different instances of a class but also across classes and to unseen arrangements of objects.
Self-supervised video object segmentation using integration-augmented attention

While supervised learning approaches show great vitality and effectiveness in video object segmentation, most of them require large amounts of annotations which are expensive and time-consuming. Recently, self-supervised learning has attracted great attention by benefiting from unlabeled video sequences. However, current patch-based self-supervised video object segmentation methods only discriminate the patch from the entire image without distinguishing the object of interest from meaningless backgrounds or even occlusion. These disturbances deteriorate the extracted features and hinder the robustness of tracking when applied to real-world video sequences. In this paper, we propose a novel model named Tracker With Integration-Augmented Attention (TWIAA) to achieve both label-free and prominent performance. Specifically, we integrate both spatial and channel dimensions by introducing a feature spatial enhancement module and a two-stream channel module. With the combination of the two modules, the network can focus on exploring the discriminative object and suppressing the irrelevant part to improve the tracking robustness. Moreover, unlike other methods that calculate features separately on the search branch and template branch, the two designed modules coupled with the Siamese network compute the respective features of the search branch and the template branch jointly to augment the interdependence of the two branches. Such interdependence is injected into both spatial and channel dimensions. So that our approach establishes richer and more discriminative associations to identify the object more accurately. In addition, our method takes full advantage of cycle-consistency information in consecutive frames, which uses coherence as the learning signal to acquire object-oriented relationships. Extensive experiments and ablation studies are conducted on large VOS benchmarks, including DAVIS-2017, YouTube-VOS-2018, and YouTube-VOS-2019. The results verify that our proposed framework has both strong feature representation and competitive performance compared with supervised and self-supervised models.
Correction adaptive square-root cubature Kalman filter with application to autonomous vehicle target tracking

For a state estimation problem of nonlinear system, the square-root cubature Kalman filter (SCKF) is an effective method when the noise statistical characteristics are known. However, the performance of SCKF often degrade significantly in the face of uncertain noises interference, particularly in case of measurement or system failure. In this paper, we focus on improving the accuracy and robustness of SCKF under irregular noise. First, a weighted adaptive SCKF (WASCKF) algorithm is presented with moving window method. The WASCKF can improve the accuracy of SCKF by adaptively adjusting the covariances of measurement noise and process noise. Next, in order to further improve the robustness of WASCKF against the abrupt abnormal noise, a correction adaptive SCKF (CASCKF) algorithm based on fault detection mechanism is proposed. The CASCKF algorithm can detect whether there is a fault according to a statistical function of Chi-square distribution, and can judge and carry out the necessary correction processing by using an isolate rule. Finally, the performance of CASCKF is verified by numerical experiments of autonomous vehicle target tracking problem. The results show that the proposed CASCKF algorithm has good accuracy and robustness even with sudden abnormal noise interference.
Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction

The graph attention network (GAT) [1] has started to become a mainstream neural network architecture since 2018, yielding remarkable performance gains in various natural language processing (NLP) tasks. Although GAT has reached the state-of-the-art (SOTA) performance as a recent success in relation prediction in knowledge graph, the current model is still limited by the following two aspects: (1) the existing model only considers the neighbors from the inbound-direction of the given entity, but ignores the rich neighborhood information from outbound-directions; (2) the existing model only uses the k-th hop output to learn the multi-hop embeddings, which leads to the loss of a large amount of early-stage embedding information (e.g., one-hop) at the graph attention step. In this study, we propose a novel bidirectional graph attention network (BiGAT) to learn the hierarchical neighbor propagation. In our proposed BiGAT, an inbound-directional GAT and an outbound-directional GAT are introduced to capture sufficient neighborhood information before propagating the bidirectional neighborhood information to learn the multi-hop feature embeddings in a hierarchical manner. Experiments conducted on the four publicly available datasets show that BiGAT achieves the competitive results in comparison to other SOTA methods.
Destruction and Reconstruction Learning for Facial Expression Recognition

The most discriminative expression features are mostly concentrated in local key facial regions. Thus, we propose a simple and efficient framework that can learn more discriminative expression features from scrambled facial images. Specifically, we first divide the input image into local subregions of the same size and shuffle them randomly at a certain range to obtain the damaged image to increase the difficulty of recognition. Then, the original image and the damaged image are fed to the network. A channel attention module is exploited for highlighting the effective features and suppressing irrelevant features. Simultaneously, during the reconstruction phase, a region alignment model is appended to establish the semantic correlation between each subregion, aiming at restoring the original spatial layout of local subregions in the original image. Extensive experiments on the RAF-DB and the FERPlus datasets demonstrate that our proposed method significantly outperformed state-of-the-art methods without any external facial expression pretraining.
Go Gig or Go Home: Enabling Social Sensing to Share Personal Data with Intimate Partner for the Health and Wellbeing of Long-Hour workers

Maintaining an awareness of one's well-being and making work-related decisions to achieve work-life balance is critical for flexible long-hour workers. In this study, we propose that social sensing could address bottlenecks in worker's awareness, interpretation of the informatics, and subsequent behavioral change. We conducted a four-week technology probe study by recruiting flexible long-hour professional drivers (Taxi and Uber drivers) and their significant others to use a social sensing prototype which collects data from the drivers and shares it with their partners as well as incorporates partners' observations. We interviewed them before and after the probe study and found that while technological sensing was able to increase drivers' awareness of their well-being status and intention to modify behaviors. The "social sensing" design was able to further shape such awareness or intention into action, highlighting the potential of using the sociotechnical approach in promoting work-life balance among long-hour workers.
The Role of Formalism in System Requirements

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision. To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as “formal.” Many exist, differing in their style, scope, and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods. The analysis uses a set of nine complementary criteria, such as level of abstraction, tool availability, and traceability support. It classifies the approaches into five categories based on their principal style for specifying requirements: natural-language, semi-formal, automata/graphs, mathematical, and seamless (programming-language-based). It includes examples from all of these categories, altogether 21 different approaches, including for example SysML, Relax, Eiffel, Event-B, and Alloy. The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches.