Reviewerly/exHarmony huggingface dataset:
Improving video ranking on social video platforms

With the emerging growth of the internet, various social video platforms are coming into existence. There are some old video platforms like YouTube and other platforms that are trending like TikTok (previously known as musical.ly), Likee app, Instagram, etc. Entering the right keywords is a challenge for the user while uploading videos. Using the right and suitable keywords can get more views on the video. Our paper mentions the algorithm that pulls accurate keywords from the video title or any search string, improving the ranking on video platforms.
Emotion Recognition and Sentiment Analysis of Extemporaneous Speech Transcriptions in Russian

Speech can be characterized by acoustical properties and semantic meaning, represented as textual speech transcriptions. Apart from the meaning content, textual information carries a substantial amount of paralinguistic information that makes it possible to detect speaker’s emotions and sentiments by means of speech transcription analysis. In this paper, we present experimental framework and results for 3-way sentiment analysis (positive, negative, and neutral) and 4-way emotion classification (happy, angry, sad, and neutral) from textual speech transcriptions in terms of Unweighted Average Recall (UAR), reaching 91.93% and 88.99%, respectively, on the multimodal corpus RAMAS containing recordings of Russian improvisational speech. Orthographic transcriptions of speech recordings from the database are obtained using available pre-trained speech recognition systems. Text vectorization is implemented using Bag-of-Words, Word2Vec, FastText and BERT methods. Investigated machine classifiers include Support Vector Machine, Random Forest, Naive Bayes and Logistic Regression. To the best of our knowledge, this is the first study of sentiment analysis and emotion recognition for both extemporaneous Russian speech and RAMAS data in particular, therefore experimental results presented in this paper can be considered as a baseline for further experiments.
KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN.
Perimeter Surveillance Based on Set-Invariance

A solution to the perimeter surveillance problem for one intruder and multiple surveillance robots based on set-invariance is presented. The surveillance robots, constrained to move on the perimeter of a polygonal region, intercept the intruders as they cross the perimeter. The proposed closed-form control laws only depend on the maximum speed of the robots and their distances to the endpoints of the line segments that make the sides of the polygon. The presented results allow for groups of robots with members of different characteristics, such as size and maximum speed, to defend polygonal regions. Simulations are used to show the application and effectiveness of the theoretical results.
A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation

Unstructured Persona-oriented Dialogue Systems (UPDS) has been demonstrated effective in generating persona consistent responses by utilizing predefined natural language user persona descriptions (e.g., I am a vegan). However, the predefined user persona descriptions are usually short and limited to only a few descriptive words, which makes it hard to correlate them with the dialogues. As a result, existing methods either fail to use the persona description or use them improperly when generating persona consistent responses. To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses. PEE consists of two main modules: persona exploration and persona exploitation. The former learns to extend the predefined user persona description by mining and correlating with existing dialogue corpus using a variational auto-encoder (VAE) based topic model. The latter learns to generate persona consistent responses by utilizing the predefined and extended user persona description. In order to make persona exploitation learn to utilize user persona description more properly, we also introduce two persona-oriented loss functions: Persona-oriented Matching (P-Match) loss and Persona-oriented Bag-of-Words (P-BoWs) loss which respectively supervise persona selection in encoder and decoder. Experimental results show that our approach outperforms state-of-the-art baselines, in terms of both automatic and human evaluations.
Minimizing the Two-Round Tweakable Even-Mansour Cipher

In CRYPTO 2015, Cogliati et al. have proposed one-round tweakable Even-Mansour (1-TEM) cipher constructed out of a single n-bit public permutation $$\pi $$ and a uniform and almost XOR-universal hash function H as $$(k, t, x) \mapsto \textsf {H}_k(t) \oplus \pi (\textsf {H}_k(t) \oplus x)$$ , where t is the tweak, and x is the n-bit message. Authors have shown that its two-round extension, which we refer to as 2-TEM, obtained by cascading 2-independent instances of the construction gives 2n/3-bit security and r-round cascading gives $$rn/r+2$$ -bit security. In ASIACRYPT 2015, Cogliati and Seurin have shown that four-round tweakable Even-Mansour cipher, which we refer to as 4-TEM, constructed out of four independent n-bit permutations $$\pi _1, \pi _2, \pi _3, \pi _4$$ and two independent n-bit keys $$k_1, k_2$$ , defined as $$k_1 \oplus t \oplus \pi _4(k_2 \oplus t \oplus \pi _3(k_1 \oplus t \oplus \pi _2(k_2 \oplus t \oplus \pi _1(k_1 \oplus t \oplus x)))), $$ is secure upto $$2^{2n/3}$$ adversarial queries. In this paper, we have shown that if we replace two independent permutations of 2-TEM (Cogliati et al., CRYPTO 2015) with a single n-bit public permutation, then the resultant construction still guarantees security upto $$2^{2n/3}$$ adversarial queries. Using the results derived therein, we also show that replacing the permutation $$(\pi _4, \pi _3)$$ with $$(\pi _1, \pi _2)$$ in the above equation preserves security upto $$2^{2n/3}$$ adversarial queries.
Deep Learning in Face Synthesis: A Survey on Deepfakes

Deepfake stemming from the combined words of "deep learning" and "fake", refers to a type of fake images and video generation technology based on artificial intelligence. In recent years, with the continuous development of deep learning, especially auto-encoders and generative adversarial networks, deepfake has made tremendous progress, resulting in the emergence of some easy-to-use application software in the market, reducing the application difficulty of face-synthesis technology. This paper provides a thorough review of deepfake, introducing its development in the past three years.
Feature Detection and Matching With Linear Adjustment and Adaptive Thresholding

Image feature detection and matching technologies are crucial aspects in machine vision. However it is still facing the dilemma between fast operation for real-time application and robust matching. To address this issue, we propose a robust and relatively fast method for image feature extraction and matching with linear adjustment and adaptive thresholding (LAAT) in this paper. The major challenge of this method is reducing the sensitivity to the brightness. To solve this problem, we adopt brightness and contrast adjustment for image pairs processed by Gaussian filtering. An adaptive thresholding FAST approach is applied for feature selection to improve the performance. The proposed method is compared with the traditional and state-of-the-art extraction methods on public dataset. Particularly, this paper focuses on the illumination change, image blur, and image rotation aspects. Experiments show that our proposed algorithm is superior to other algorithms in the comprehensive evaluation of various parameters, especially for illumination and blur transformations.
An analysis of digital identity management systems - a two-mapping view

Digital identity is essential to almost all information systems. This paper provides a new perspective for reducing digital identity management systems (DIMS) to two mappings, the core operations on the mappings, and the trust model built around the mappings. Using this two-mapping view, we derive criteria for determining whether a DIMS solution is self-sovereign. We also compare decentralized identity management solutions with the traditional centralized identity management solutions. From the comparison and analysis, we have the following findings. The differences between decentralized and centralized solutions stem from the trust model differences which dictate storage scheme differences, and therefore other operation-level differences. With a more trustless trust model and storage scheme, current decentralized solutions can be promising candidates for self-sovereign DIMS. Design considerations and certain limitations of the current decentralized solutions are analyzed based on the two-mapping view as well, which can provide useful directions to further study.
Knowledge Graphs

Knowledge Graphs can be considered as fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learning, among others, Knowledge Graphs have rapidly gained popularity in academia and industry in the past years. The integration of such disparate disciplines and techniques give the richness to Knowledge Graphs, but also present the challenge to practitioners and theoreticians to know how current advances develop from early techniques in order, on one hand, take full advantage of them, and on the other, avoid reinventing the wheel. This tutorial will provide a historical context on the roots of Knowledge Graphs grounded in the advancements of Logic, Data and the combination thereof.
Speaking Speed Control of End-to-End Speech Synthesis Using Sentence-Level Conditioning

This paper proposes a controllable end-to-end text-to-speech (TTS) system to control the speaking speed (speed-controllable TTS; SCTTS) of synthesized speech with sentence-level speaking-rate value as an additional input. The speaking-rate value, the ratio of the number of input phonemes to the length of input speech, is adopted in the proposed system to control the speaking speed. Furthermore, the proposed SCTTS system can control the speaking speed while retaining other speech attributes, such as the pitch, by adopting the global style token-based style encoder. The proposed SCTTS does not require any additional well-trained model or an external speech database to extract phoneme-level duration information and can be trained in an end-to-end manner. In addition, our listening tests on fast-, normal-, and slow-speed speech showed that the SCTTS can generate more natural speech than other phoneme duration control approaches which increase or decrease duration at the same rate for the entire sentence, especially in the case of slow-speed speech.
Towards a Better Understanding of Label Smoothing in Neural Machine Translation

In order to combat overfitting and in pursuit of better generalization, label smoothing is widely applied in modern neural machine translation systems. The core idea is to penalize over-confident outputs and regularize the model so that its outputs do not diverge too much from some prior distribution. While training perplexity generally gets worse, label smoothing is found to consistently improve test performance. In this work, we aim to better understand label smoothing in the context of neural machine translation. Theoretically, we derive and explain exactly what label smoothing is optimizing for. Practically, we conduct extensive experiments by varying which tokens to smooth, tuning the probability mass to be deducted from the true targets and considering different prior distributions. We show that label smoothing is theoretically well-motivated, and by carefully choosing hyperparameters, the practical performance of strong neural machine translation systems can be further improved.
10 Like, Share and Comment! The Use of Facebook by Public Libraries and Museums: A Case Study from Tromsø, Norway

2 See for instance the Norwegian government's white paper number 49 (2008)(2009).Framtidas museum -Forvaltning, forskning, formidling, fornying, 102: "Museene skal gi både kunnskap og opplevelse.De skal vaere tilgjengelige for alle og vaere relevante og aktuelle samfunnsinstitusjoner som fremmer kritisk refleksjon og skapende innsikt.En aktiv formidling er derfor viktig både i et demokratiperspektiv og i et allment kulturperspektiv" (The museums will provide both knowledge and experience.They should be accessible to all and be relevant community institutions that promote critical reflection and creative insight.Active dissemination is therefore important both in a democracy perspective and in a general cultural perspective (my translation)). 3 See for instance the Norwegian government's white paper number 24 (2008)(2009) about the digital preservation and dissemination of cultural heritage, and white paper number 27 (2015)(2016) about the use of information technology to make everyday life easier.Public libraries are supposed to increase the digital skills of the inhabitants, and thus contribute to increased digital user participation (Kommunal-og moderniseringsdepartementet (2015), 116-117).
RETRACTED: Practice of Hybrid Teaching Mode of English Writing Based on Artificial Intelligence

Abstract Based on the analysis of artificial intelligence teaching materials,the hybrid education mode of artificial intelligence provides a new teaching idea for schools that have not yet set up the hybrid education mode of artificial intelligence.This paper mainly studies the hybrid education of English writing based on artificial intelligence.This paper mainly describes the application of hybrid education model in English writing teaching.Taking a class as an experimental class,using the theory of teaching design,combined with the specific situation of class students’ English writing teaching,a hybrid teaching model of English writing based on artificial intelligence is designed.Based on the analysis of students’ growth characteristics and cultural basis,combined with the analysis of foreign classical teaching modes,this paper puts forward a hybrid teaching mode suitable for students’ AI courses by using constructivism teaching mode,cooperative teaching mode and mastery learning mode. This paper mainly uses the teaching recommendation algorithm of artificial intelligence,through the intelligent statistics and analysis of the courses that students are interested in,recommends when and what courses the environment teachers should teach.The experimental results show that under the mixed teaching mode based on artificial intelligence,students’ English writing level has increased by 40%compared with 2016,and the teaching efficiency under artificial intelligence has increased by 35% in 2016.
Augmented Reality Model Framework for Maritime Education to Alleviate the Factors Affecting Learning Experience

The maritime industry in the Philippines had several deficiencies that must be addressed seriously in order to maintain strong reputation in producing competitive seafarers worldwide. This affects student’s educational skills and knowledge before finishing their education and training. This research led in identifying several factors affecting learning experiences to be addressed by incorporating advance technologies to further enhance student’s learning experiences. The learning strategies used in this study are focused on using presentation, multimedia, simulator and hands-on. But these are not enough to satisfy students craving to learn due to some constraints during learning. In this study most of the factors identified are from literatures, monitoring reports, previous surveys and studies conducted in one of the maritime institution north of the Philippines. A descriptive research method was applied with the 32 marine engineering third year students as the respondents. Results extracted from questionnaires revealed important points that must be considered on the different learning strategies. The learning experiences of students on the different learning strategy used in the learning process exposed several learning factors to be regulated. The findings suggest that there is still an opportunity of improvement on student’s learning experiences by introduction emerging advance technology by developing an augmented reality model framework to enhance learning experiences.
Realizing the Role of Permissioned Blockchains in a Systems Engineering Lifecycle

A key requirement for an integrated digital tool chain is secure access and control of data assets. Not all stakeholders will have the same access to or control over the flow of information, some will be able to input or change data whilst others will only be able to read the data. Simply providing secure access protocols is not sufficient because copied data can quickly become disassociated and modified from its original instantiation, leading to its reuse elsewhere or later in the lifecycle but in an inappropriate way. Therefore, data management mechanisms are required that capture information about the data along with any decisions or modifications it has undergone during the course of its life, thus providing complete traceability for later validation purposes. This undertaking is essential across the systems engineering lifecycle. This pursuit involves controlling who can access and modify data within the lifecycle. This paper describes a solution to this by the introduction of blockchain technology, a relatively new technology that allows digital information to be distributed but not copied, making it an immutable set of time-stamped data managed by a network of connected systems and services. Though blockchain technology is not commonly referred to when discussing Industry 4.0, the technology’s capabilities should add value when applied in a context of data management and security within the lifecycle of a product or services and in conjunction with digital twins, big data, and IoT. This paper describes how permissioned blockchains can be implemented within a systems engineering lifecycle, providing example architecture patterns showing how data provenance can be maintained throughout.
A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power.However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN into making false predictions.The existing methods of generating adversarial examples include pixel-level perturbation or spatial transformation of images, which cannot consider concurrently with the semantic quality of adversarial examples or success rate of attack.These methods are computationally bulky and slow to generate the adversarial examples.To solve this kind of issue, a two-stage generative adversarial networks (TSGAN) with semantic content constraints is proposed in this paper.The first-stage uses the original example dataset to train generator G, which can help the generator learn the distribution of real examples.Then, the example semantic quality constraint loss function, the adversarial loss function and the distance loss function are adopted in the second-stage, so that the generator G can continue to learn to search the distribution of the adversarial examples, and train the new generator G adv .The adversarial examples generated by generator G adv are better fit the distribution of real examples, and have targeted black-box attack capability.The experiments show that the adversarial examples generated by TSGAN can achieve the success rate of attack at 98.40% in target model, 29.40% success rate in defense-oriented model.And 77.58% success rate is obtained in the transfer test attack.The results show that the adversarial examples generated by the proposed model, which has a highly attack success rate and more difficult to defense.Meanwhile, the improved adversarial examples have stronger transfer ability than the existing models.The proposed model can effectively reduce the expression of target category features of the adversarial examples, and the generated adversarial examples have better semantic quality than others.
Pruning Filter in Filter

Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pruning (FP) and weight pruning (WP). FP wins at hardware compatibility but loses at the compression ratio compared with WP. To converge the strength of both methods, we propose to prune the filter in the filter. Specifically, we treat a filter $F \in \mathbb{R}^{C\times K\times K}$ as $K \times K$ stripes, i.e., $1\times 1$ filters $\in \mathbb{R}^{C}$, then by pruning the stripes instead of the whole filter, we can achieve finer granularity than traditional FP while being hardware friendly. We term our method as SWP (\emph{Stripe-Wise Pruning}). SWP is implemented by introducing a novel learnable matrix called Filter Skeleton, whose values reflect the shape of each filter. As some recent work has shown that the pruned architecture is more crucial than the inherited important weights, we argue that the architecture of a single filter, i.e., the shape, also matters. Through extensive experiments, we demonstrate that SWP is more effective compared to the previous FP-based methods and achieves the state-of-art pruning ratio on CIFAR-10 and ImageNet datasets without obvious accuracy drop. Code is available at this https URL
Rate-Invariant Modeling in Lie Algebra for Activity Recognition

Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)×…×SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches.