Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Short Video Recommendation Algorithm Based on Latent Factor Model

With the continuous Internet development, network information amount has increased significantly. In this way, users faced with extensive network information cannot quickly find information that meets their needs, leading to decreased efficiency in network information usage. Accordingly, personalized recommendation system should be an important part of the electronic platform system as recommendation efficiency directly affects the user experience and the use of electronic platforms. In recent years, the data of electronic platforms presents a trend of massive growth, resulting in decreased accuracy in system recommendations, increased errors, and decreased efficiency. Therefore, it is particularly necessary to study and analyze personalized recommendation algorithms. Based on the research and application of latent factor model in short video recommendation algorithm, this paper aims to recommend users with video content that truly meets user needs and interests.
Forensicability of Deep Neural Network Inference Pipelines

We propose methods to infer properties of the execution environment of machine learning pipelines by tracing characteristic numerical deviations in observable outputs. Results from a series of proof-of-concept experiments obtained on local and cloud-hosted machines give rise to possible forensic applications, such as the identification of the hardware platform used to produce deep neural network predictions. Finally, we introduce boundary samples that amplify the numerical deviations in order to distinguish machines by their predicted label only.
Evolving Quantized Neural Networks for Image Classification Using A Multi-Objective Genetic Algorithm

Recently, many model quantization approaches have been investigated to reduce the model size and improve the inference speed of convolutional neural networks (CNNs). However, these approaches usually inevitably lead to a decrease in classification accuracy. To address this problem, this paper proposes a mixed precision quantization method combined with channel expansion of CNNs by using a multi-objective genetic algorithm, called MOGAQNN. In MOGAQNN, each individual in the population is used to encode a mixed precision quantization policy and a channel expansion policy. During the evolution process, the two polices are optimized simultaneously by the non-dominated sorting genetic algorithm II (NSGA-II). Finally, we choose the best individual in the last population and evaluate its performance on the test set as the final performance. The experimental results of five popular CNNs on two benchmark datasets demonstrate that MOGAQNN can greatly reduce the model size and improve the classification accuracy at the same time.
Inpainting of Vintage Films Based on Variational Auto-encoder

There are many existing video restoration frameworks, but there are no professional methods to deal with multiple degradation restorations of vintage films. With the help of the idea of mapping, this paper constructs a model with two variational autoencoders to repair the complex degradation problem. In addition, convolutional LSTM is used for video de-flicker processing to obtain smoother and natural results. The experimental results prove that this method can repair different damages in the vintage films screen at the same time, and achieve good results.
Using Scene Graphs for Detecting Visual Relationships

In this paper we solve the problem of detecting relationships between pairs of objects in an image. We develop spatially aware word embeddings using scene graphs and use joint feature representations containing visual, spatial and semantic embeddings from the input images to train a deep network on the task of relationship detection. Further, we propose to utilize context aligned scene graph embeddings from the train set, without requiring explicit availability of scene graphs at test time. We show that the proposed method outperforms the state-of-the-art methods for predicate detection and provides competing results on relationship detection. We also show the generalization ability of the proposed method by performing predictions under zero shot settings. Further, we also provide an exhaustive empirical evaluation on each component of the proposed network.
Secure Determining of the k-th Greatest Element Among Distributed Private Values

One of the basic operations over distributed data is to find the k-th greatest value among union of these numerical data. The challenge arises when the datasets are private and their owners cannot trust any third party. In this paper, we propose a new secure protocol to find the k-th greatest value by means of secure summation sub-protocol. We compare our proposed protocol with other similar protocols. Specially, we will show that our scheme is more efficient than the well-known protocol of Aggarwal et.al. (2004) in terms of computation and communication complexity. Specifically, in the case of T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = 1 secret value for any party P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> our protocol has log m computation overhead and δ log m communication overhead for party P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , where m and δ are the maximum acceptable value and communication overhead of the secure summation sub-protocol, respectively. The overheads of our protocol is exactly half of the overheads of Aggarwal's protocol.
Super-Resolution Reconstruction of Human Face Image Based on Network of Residual Dense Connection Channel Attentional

Convolutional neural network is very important for super-resolution reconstruction of face images. However, deeper convolutional networks are more difficult to train for face super-resolution reconstruction. The input low-resolution face images contain a wealth of low-frequency information, these low-frequency information is processed equally in the channel, which hinders the characterization ability of the convolutional neural network, sequentially the recovery performance is affected, so that the super-resolution reconstruction results obtained are inaccurate. In order to solve these problems, we propose a residual convolution network with a compact structure. Specifically, we propose a modified residual structure group to deepen the network structure. In the residual structure group, the compact and dense connection structure is combined to make full use of the high frequency information learned by the previous network, and the features of the shallow small perception field are transmitted to the network to complete the specific task. In addition, we introduce an attentional mechanism for adaptive re-estimation of channel characteristics by considering the dependencies between channels. Experiment results show that the proposed network achieves better accuracy and satisfactory visual effect.
The Digital Divide in the World of Education at the Time of COVID-19

Education has always been a challenge, today even more so given the difficulties of the moment, to support school in the time of COVID-19. Schools at every level have reorganized the training offer, implementing technological solutions. All this has raised a problem: the division is no longer between those who have access to the network and those who do not, but between those who know and those who do not know these new tools. Lately the concept of digital divide, understood as a gap of opportunity between those who have and those who do not have digital skills, has evolved into the concept of digital inequality. Given these premises, the work investigates after careful analysis, the impact of digital systems on the educational system, such as the emergence of the “digital divide,” impacts on training within universities. To verify the “digital divide,” the work proposes a quantitative survey carried out through a questionnaire submitted to a sample of students. The subject of the survey is given by the students of the University of Calabria.
Characteristics and Enlightenment of MIT Computer Science and Engineering Courses Based on Analytic Hierarchy Process Model

Computer science and engineering is one of the first-class majors of MIT. The study aims to find out the enlightenment of MIT's computer science and engineering curriculum and talent training model for other modern universities. Based on the analytic hierarchy process model, this paper analyzes the model and characteristics of the setting of MIT computer science and engineering courses and summarizes the enlightenment of courses setting model, which has strong significance for optimizing the engineering curriculum system and improving the quality of talent training of other modern universities.
Learning Natural Thresholds for Image Ranking

For image ranking tasks with naturally continuous output, such as age and scenicness estimation, it is common to discretize the label range and apply methods from (ordered) classification analysis. In this paper, we propose a data-driven approach for simultaneous representation learning and label discretization. Compared to arbitrarily selecting thresholds, we seek to learn thresholds and image representations by minimizing a novel loss function in an end-to-end model. We demonstrate our combined approach on a variety of image ranking tasks and demonstrate that it outperforms task-specific methods. Additionally, our learned partitioning scheme can be transferred to improve methods that rely on discretization.
Iterative quantum phase estimation on an IBM quantum processor

Abstract An elementary algorithm of quantum phase estimation based on the modified Kitaev algorithm is implemented on two qubits of an IBM quantum processor. This algorithm includes adiabatic preparation of the initial state, controlled phase shift with allowance for the results of previous measurements of qubit states, and single measurement of the qubit quantum state in order to obtain each significant bit of the measured phase. Classical error correction is applied to determine the correct sequence of bits, which makes it possible to eliminate the influence of limited accuracy of two-qubit gates.
Russian and Foreign Postgraduate Students in a Technical University: Problematic Fields of Online Learning

This research continues our study of educational communications in the context of active implementation of digital technologies in Russian higher education. The topic of this report is the evaluation by russian and foreign postgraduate students of online distance learning classes in general education disciplines that are part of the postgraduate curriculum of a Russian technical university. The aim of the study is to catalogue the communicative and content problems identified in the total transition of the postgraduate education process to an online format.
Improving Word Recognition using Multiple Hypotheses and Deep Embeddings

We propose a novel scheme for improving the word recognition accuracy using word image embeddings. We use a trained text recognizer, which can predict multiple text hypothesis for a given word image. Our fusion scheme improves the recognition process by utilizing the word image and text embeddings obtained from a trained word image embedding network. We propose EmbedNet, which is trained using a triplet loss for learning a suitable embedding space where the embedding of the word image lies closer to the embedding of the corresponding text transcription. The updated embedding space thus helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). The CAB module takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings to generate an updated distance vector. The updated distance vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books in the Hindi language. Our method achieves an absolute improvement of around 10% in terms of word recognition accuracy.
GrainSynth: A Generative Synthesis Tool Based on Spatial Interpretations of Sound Samples

This paper proposes a generative design approach for the creative exploration of dynamic soundscapes that can be used to generate compelling and immersive sound environments. A granular synthesis tool is considered based on the perceptual self-organization of sound samples by utilizing the t-Stochastic Neighboring Embedded algorithm (t-SNE) for the spatial mapping of sonic grains into a 2D space. The proposed system was able to relate the visual stimuli with the sonic responses in the context of the generic gestalt principles of visual perception. According to user evaluation, the application operated intuitively and also revealed the potential for creative expressiveness both from the user’s perspective and as a standalone, generative synthesizer.
Performance Analysis of GPU-CPU for The Face Detection

The face detection process is one of the research objects in the field of image processing which aims to recognize faces from an image. Performance and face detection accuracy can be measured based on the platform used. In this research, we propose a face detection approach using the Viola-Jones algorithm to perform face detection that is applied to CPU and GPU programs utilizing the architecture of the Compute Unified Device Architecture (CUDA). The image used in this study is an image in RGB format with 25 different image resolution sizes. Test results for each platform are the detection processing time on the CPU is 1433048.72 ms with an accuracy of 90% while the GPU is 233374.583 ms with 72% of accuracy.
Study on Soil Heavy Metal Contamination and Risk Estimation in Sixian

Abstract At present, heavy metal contamination of soils is a extensive attention on account of the develop rapidly of urbanization and industrialization. In this paper, topsoil samples were gathered and the contents of heavy metals for example: Cd, Cr, Cu, Zn, As and Pb were determined in Sixian City. The contamination degree of heavy metals and potential ecological risk were estimated using uniparted factor contamination index, Nemero integrated contamination method, geo-accumulation index, and potential ecological risk method. The results showed: except As, mean concentrations of Pb, Zn, Cu, Cr and Cd were greater than soil background values in Sixian City. The coefficient of variation of Zn, Pb, Cu and As are greater than 30%, suggesting that they were more probably affected by human disturbance in the soil of Sixian City. The Pc values ranged between 1.46 and 26.83, displaying almost whole survey region is varying degrees contamination because of heavy metals. The I geo showed that the average values implied the follow contamination order: Pb &gt; Cd &gt; Cu &gt; Zn &gt; As &gt; Cr. The most serious element is Pb. The E i values of Cu, Zn, As and Cr were observed in the ranges 5.66 to 21.15, 0.74 to 3.13 and 3.99 to 39.42, 1.54 to 3.57, respectively. The RI values were less than 150 with 85% light potential ecological risk and 15% moderate risk.
Review on Deep Adversarial Learning of Entity Resolution for Cross-Modal Data

With the repaid development of the Internet, multimedia data such as image, text, video, audio is increasing, which brings opportunities and challenges to the development of the economy and science. Cross-modal data entity resolution aims to find different objective descriptions of the semantically similar items from objects in different modalities. However, different modality data have the features with underlying heterogeneity and high-level semantic related. Starting from the problem of modality gap between cross-modal data, this paper introduces how to use the idea of confrontational learning to solve the cross-modal data entity resolution problem between images and text from the aspects of feature extraction and emotional state association.
Progressive Dialogue State Tracking for Multi-Domain Dialogue Systems

There are two critical observations in multi-domain dialogue state tracking (DST) ignored in most existing work. First, the number of triples (domain-slot-value) in dialogue states generally increases with the growth of dialogue turns. Second, although dialogue states are accumulating, the difference between two adjacent turns is steadily minor. To model the two observations, we propose to divide the task into two successive procedures: progressive domain-slot tracking and shrunk value prediction. Specifically, domain-slot pairs are first modeled in a multi-level structure that can be predicted progressively based on previous turns. Then, we employ a generative approach to producing dialogue values for the predicted, rather than for all possible, domain-slot pairs. This divide-and-conquer approach not only enables parallelization for predicting domain-slot pairs, but also significantly reduces the number of domain-slot candidates for value prediction. Experimental results on the MultiWOZ datasets confirm that our methodology (PRO-DST) achieves very favorable improvement over existing methods.
Properties of transient spectrum and field purity for a qubit system in squeezed states

The present study investigates the transient spectrum (TS) of a two-level system that interacts with a generalized squeezed state without using the rotating wave approximation (RWA). We consider the purity of the optical field, which is developed in a generalized squeezed state, utilizing the linear entropy. The analytic expressions of the TS and field purity (FP) of the bipartite system are evaluated. Moreover, the study exhibits the influence of the squeezed parameter and some photons transition on the TS and FP. We obtain that the control of the FP may be generated based on an adequate choice of the photons transition and squeezed parameter. Such results can be utilized in the understanding and development of various tasks of quantum physics and optics.
Predicting Sentiments to an accuracy matching the gesture recognized for the specially-abled

Sentiment analysis being a polarity based detection technique, is going around in the academic research and industry domain for quite a long time, not only seeing the economic benefits to both fields alike, but also the significance in understanding human behavior. In this work, Natural Language Processing (NLP) is done to accurately recognize the sentiment behind the gestures of a specially abled individual, to analyse their behavior in real time considering different application platforms.
RFCC: Random Forest Consensus Clustering for Regression and Classification

Random forests are invariant and robust estimators that can fit complex interactions between input data of different types and binary, categorical, or continuous outcome variables, including those with multiple dimensions. In addition to these desirable properties, random forests impose a structure on the observations from which researchers and data analysts can infer clusters or groups of interest. These clusters not only provide a structure to the data at hand, they also can be used to elucidate new patterns, define subgroups for further analysis, derive prototypical observations, identify outlier observations, catch mislabeled data, and evaluate the performance of the estimation model in more detail.We present a novel clustering algorithm called Random Forest Consensus Clustering and implement it in the Scikit-Learn / SciPy data science ecosystem. This algorithm differs from prior approaches by making use of the entire tree structure. Observations become proximate if they follow similar decision paths across trees of a random forest. We illustrate why this approach improves the resolution and robustness of clustering and that is especially suited to hierarchical approaches.
Funding the PLE Factory in a Multi‐Customer Contract‐Based PLE Organization

ABSTRACT Feature‐based Product Line Engineering is a well‐defined, repeatable, automation‐centric PLE method that is delivering even improvements in time, cost, and quality. An organization intent on adopting it so they can reap the benefits for their product line or product lines needs a viewpoint focusing on the people involved and what they do to keep the PLE factory operational on a day‐to‐day basis. This article describes an organizational structure for Feature‐Based PLE based on the factory concept. It introduces the few roles that have no analog in other development disciplines; they are new to Feature‐Based PLE. It also describes how traditional systems engineering roles carry out traditional systems engineering tasks, but with slight PLE‐inspired extensions. Finally, we will explain why these changes are necessary.
NN-KOG2P: A Novel Grapheme-to-Phoneme Model for Korean Language

With the development of text-to-speech technology, high-quality voices can be heard in AI speaker responses, car navigation guidance, and news article-reading services. As services become more diverse, domains are expanded, requiring fast and high-performance grapheme-to-phoneme (G2P) technology. In this paper, we propose a novel Korean G2P model architecture, reflecting the characteristics of Korean pronunciation, called neural network-based Korean G2P (NN-KoG2P). Our proposed method achieves high accuracy in an open-domain dataset and a fast inference speed that can generate pronunciation sequences in real-time services.
PEAN: 3D Hand Pose Estimation Adversarial Network

Despite recent emerging research attention, 3D hand pose estimation still suffers from the problems of predicting inaccurate or invalid poses which conflict with physical and kinematic constraints. To address these problems, we propose a novel 3D hand pose estimation adversarial network (PEAN) which can implicitly utilize such constraints to regularize the prediction in an adversarial learning framework. PEAN contains two parts: a 3D hierarchical estimation network (3DHNet) to predict hand pose, which decouples the task into multiple subtasks with a hierarchical structure; a pose discrimination network (PDNet) to judge the reasonableness of the estimated 3D hand pose, which back-propagates the constraints to the estimation network. During the adversarial learning process, PDNet is expected to distinguish the estimated 3D hand pose and the ground truth, while 3DHNet is expected to estimate more valid pose to confuse PDNet. In this way, 3DHNet is capable of generating 3D poses with accurate positions and adaptively adjusting the invalid poses without additional prior knowledge. Experiments show that the proposed 3DHNet does a good job in predicting hand poses, and introducing PDNet to 3DHNet does further improve the accuracy and reasonableness of the predicted results. As a result, the proposed PEAN achieves the state-of-the-art performance on three public hand pose estimation datasets.
Unsupervised Hard Case Extraction Based on Image Perceptual Hash Encoding

The camera-based object detection algorithm is essential in autonomous driving. If the object is not detected when the vehicle is driving on the highway, it will cause a severe safety hazard. To evaluate and improve the object detection model, we propose a hard case extraction algorithm based on image perceptual hash encoding. We encode the object regions of each frame and then match them in adjacent frames. We optimize the search algorithm to achieve fast matching, improving efficiency by about ten times while ensuring accuracy. Then, we extract hard frames from a large number of unlabeled video frames, and the experimental results show that the accuracy is 92%. It is of great significance to evaluate and improve the object detection model and expand effective data.