Reviewerly/exHarmony huggingface dataset:
Speech Drives Templates: Co-Speech Gesture Synthesis with Learned Templates

Co-speech gesture generation is to synthesize a gesture sequence that not only looks real but also matches with the input speech audio. Our method generates the movements of a complete upper body, including arms, hands, and the head. Although recent data-driven methods achieve great success, challenges still exist, such as limited variety, poor fidelity, and lack of objective metrics. Motivated by the fact that the speech cannot fully determine the gesture, we design a method that learns a set of gesture template vectors to model the latent conditions, which relieve the ambiguity. For our method, the template vector determines the general appearance of a generated gesture sequence, while the speech audio drives subtle movements of the body, both indispensable for synthesizing a realistic gesture sequence. Due to the intractability of an objective metric for gesture-speech synchronization, we adopt the lip-sync error as a proxy metric to tune and evaluate the synchronization ability of our model. Extensive experiments show the superiority of our method in both objective and subjective evaluations on fidelity and synchronization.
Evaluation of the learning process of informational competencies in university students

This article aims to present the results of a study on the learning process of informational competence in the Universidad José Vasconcelos, Oaxaca, Mexico. From a qualitative research approach, semi-structured interviews, and requests for drawings were applied, and non-participant observation of six students’ information search practices was carried out. The findings made it possible to identify quantitative and qualitative changes in the declarative, procedural and attitudinal components of the competence, which differentiate three levels of performance: low, medium, and high. The critical element to understand the competence learning process is in its attitudinal component since this determines the amount and type of declarative knowledge, as well as the procedures that the student is willing to deploy in a coordinated way to search, evaluate and use the information to achieve your academic goals.
Hardware-based security approach for secure distributed cloud data storage and retrieval

The East African Community (EAC) recognizes the fundamental role of science and technology in economic development. This led the Partner States to promote cooperation in the development and application of science and technology within the Community. The adoption of cloud computing in East Africa Community member states may induce a number of paths for Internet-based service provisioning to satisfy various demands in the community. However, data privacy and security has turned into crucial issues that resist cloud adoption for the past decades in the area of computerized systems. The dominating issue regarding security and privacy is pointed out on the data accessibility whereby cloud service providers and consumers simply gain access to secret information (data). The issue resulted in the cloud service consumer’s fear and slowed down the adoption speed of cloud computing from diverse areas of applications, i.e., governmental, medical and financial institutions; therefore, in this article we address this issue and propose a robust Hardware-Based Security (HBS) approach, whereby the cloud service provider and consumers are limited to simply access any stored data in the cloud data centers. This approach halves data files into data segments and distributes encrypted file segments to different cloud data centers. Our approach is built on Secure Data Distribution (SDD) and Data Retrieval (DR) Algorithms described in a Secure Efficient Data Distribution Model (SEDD). The analysis shows robust data security and less computation power which can resist unauthorized data accessibility with less latency. Therefore, once the East African Community member states adopt a secure cloud services, the development and application of science and technology within the community will boost the sustainable economic development.
Parallel Prefix Sum with SIMD

The prefix sum operation is a useful primitive with a broad range of applications. For database systems, it is a building block of many important operators including join, sort and filter queries. In this paper, we study different methods of computing prefix sums with SIMD instructions and multiple threads. For SIMD, we implement and compare horizontal and vertical computations, as well as a theoretically work-efficient balanced tree version using gather/scatter instructions. With multithreading, the memory bandwidth can become the bottleneck of prefix sum computations. We propose a new method that partitions data into cache-sized smaller partitions to achieve better data locality and reduce bandwidth demands from RAM. We also investigate four different ways of organizing the computation sub-procedures, which have different performance and usability characteristics. In the experiments we find that the most efficient prefix sum computation using our partitioning technique is up to 3x faster than two standard library implementations that already use SIMD and multithreading.
Video Instance Segmentation with a Propose-Reduce Paradigm

Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete results by tracking or matching. These methods may cause error accumulation in the merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step. We further build a sequence propagation head on the existing image-level instance segmentation network for long-term propagation. To ensure robustness and high recall of our proposed framework, multiple sequences are proposed where redundant sequences of the same instance are reduced. We achieve state-of-the-art performance on two representative benchmark datasets -- we obtain 47.6% in terms of AP on YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code is available at https://github.com/dvlab-research/ProposeReduce.
Few-Shot and Continual Learning With Attentive Independent Mechanisms

Deep neural networks (DNNs) are known to perform well when deployed to test distributions that shares high similarity with the training distribution. Feeding DNNs with new data sequentially that were unseen in the training distribution has two major challenges -- fast adaptation to new tasks and catastrophic forgetting of old tasks. Such difficulties paved way for the on-going research on few-shot learning and continual learning. To tackle these problems, we introduce Attentive Independent Mechanisms (AIM). We incorporate the idea of learning using fast and slow weights in conjunction with the decoupling of the feature extraction and higher-order conceptual learning of a DNN. AIM is designed for higher-order conceptual learning, modeled by a mixture of experts that compete to learn independent concepts to solve a new task. AIM is a modular component that can be inserted into existing deep learning frameworks. We demonstrate its capability for few-shot learning by adding it to SIB and trained on MiniImageNet and CIFAR-FS, showing significant improvement. AIM is also applied to ANML and OML trained on Omniglot, CIFAR-100 and MiniImageNet to demonstrate its capability in continual learning. Code made publicly available at this https URL.
An Efficient Privacy Preserving using Map Reduce based International Data Encryption Algorithm and weighted Auto Encoder

One of the most important aspects of big data investigation is data security. The majority of cloud system applications involve sensitive data, namely personal, business, or health records. Threats to such data could put the cloud platforms that store it in jeopardy. Conventional security solutions, on the other hand, are incapable of securing big data migration. To handle the generation of vast amount of data and security aspects of generated data across cloud is handled by an effective privacy preserving mechanism. Initially, cloud based dataset is clustered and information are balanced with map-reduce mechanism. Further, the information are encrypted using International Data Encryption Algorithm (IDEA) and the convolution process is attained over pertain the estimation process to the encrypted or convoluted data. The estimation process is accomplished by the weighted Auto encoder KNN (WAEKNN) classifier. Suppose the encrypted data are not appropriately encrypted means the data are again transmitted to the convolution process. Experimental results of proposed framework is compared with existing technique and the big data based privacy preserving scheme outperforms the existing techniques.
Sign Detection and Signed Integer Comparison for the 3-Moduli Set {2^n±1,2^(n+k)}

Comparison, division and sign detection are considered complicated operations in residue number system (RNS). A straightforward solution is to convert RNS numbers into binary formats and then perform complicated operations using conventional binary operators. If efficient circuits are provided for comparison, division and sign detection, the application of RNS can be extended to the cases including these operations.For RNS comparison in the 3-moduli set , we have only found one hardware realization. In this paper, an efficient RNS comparator is proposed for the moduli set which employs sign detection method and operates more efficient than its counterparts. The proposed sign detector and comparator utilize dynamic range partitioning (DRP), which has been recently presented for unsigned RNS comparison. Delay and cost of the proposed comparator are lower than the previous works and makes it appropriate for RNS applications with limited delay and cost.
Integration of Blockchain with Big Data Free Schema

Security, immutability, and nature present numerous difficulties. Big data in monetization is still among the biggest concerns for large of dataset management .Large organizations easily convert unstructured data sets into structured data sets. Still the problems are faced in smart services in searching of reliable big data solutions. Security and privacy are the main concerns of cybersecurity. The main problem of integrating blockchain with big data free schema is how big data can be transferred to the blockchain model with complete assurance of privacy, security, and integrity. It requires an architectural model that paves the way to decentralized, immutable and secure data modeling. Blockchain is relatively a new technology, guaranteeing integrity and ensuring security in the field of information systems. Its first implementation is bitcoin as a crypto consensus model in which the computational evaluation of currency has gained a lot of attention. Blockchain mining and approving exchanges comprise a distributed system that contains connections. This article opens new paths into the blocks of transactions. Immutability, authentication and distributed consensus are the main characteristics of blockchain.
Technology (Mooc) Prospective Elementary School Teachers Introducing Regional Education Figures of North Sumatera (Indonesia)

A large-scale open on line learning program (a form of technology-based learning) is conducted online to support education actors, especially teachers, to master information skills about local education leaders. So that sharing information with information and communication technology is easy to obtain. In particular, things that need to be analyzed in online learning based on information from local figures for teachers in schools are: first, how to increase public awareness of active participation, participation and independence of information about local education leaders. Second, how to understand information about local education figures, including identifying information needs, seeking information, using information and sharing information through miniature large-scale open online learning courses. Basically, the method used in this program is through developing forms of learning and research on a limited scale. Through the learning process, the next stages of knowledge transfer activities carried out through assignments and assessments will be published and ended online.
The Educational Use of a Website in the Teaching of Chemistry Module on "Acids – Bases – Salts"

This paper aims to present the development of a website that allows students to follow the theoretical material of the "Acids-Bases-Salts" subunit, enriched with images, graphics and video recorded phenomena. This way, unlike a typical school laboratory, it is permissible to study the everyday phenomena that are performing rapidly. Thus, more time is spent on the teaching activity aimed at linking phenomena and graphical representations.
Digital Transformation Of Universities: Notes On The Global Conference Edcrunch Ural On Technologies In Education

Overview. The article discusses universitiesâ€™ of digital transformation issues, which were discussed at the EdCrunch Ural global conference on technology in education held on April 24â€“26, 2019 at the Ural Federal University (Yekaterinburg). There are observed the opinions and positions of the conference participants on the general concept of a digital university, on new forms and models of student learning, on the transformation of the teacherâ€™s role, on the creation and effective use of digital educational content, and on new approaches to university management.
Noisy Text Data: Achilles' Heel of popular transformer based NLP models

In the last few years, the ML community has created a number of new NLP models based on transformer architecture. These models have shown great performance for various NLP tasks on benchmark datasets, often surpassing SOTA results. Buoyed with this success, one often finds industry practitioners actively experimenting with fine-tuning these models to build NLP applications for industry use cases. However, for most datasets that are used by practitioners to build industrial NLP applications, it is hard to guarantee the presence of any noise in the data. While most transformer based NLP models have performed exceedingly well in transferring the learnings from one dataset to another, it remains unclear how these models perform when fine-tuned on noisy text. We address the open question by Kumar et al. (2020) to explore the sensitivity of popular transformer based NLP models to noise in the text data. We continue working with the noise as defined by them -- spelling mistakes &amp; typos (which are the most commonly occurring noise). We show (via experimental results) that these models perform badly on most common NLP tasks namely text classification, textual similarity, NER, question answering, text summarization on benchmark datasets. We further show that as the noise in data increases, the performance degrades. Our findings suggest that one must be vary of the presence of noise in their datasets while fine-tuning popular transformer based NLP models.
Cyber forensics

Digital forensics is a branch within the field of forensic science that is concerned with retrieving, storing and analyzing electronic data that can be useful in digital criminal investigations. The Council of Europe Convention on Cybercrime, also called ‘Budapest Convention on Cybercrime’ or simply ‘Budapest Convention’ refers to electronic evidence as evidence that can be collected in electronic form of a criminal offence. The term Unmanned Aerial Vehicle (UAV) popular and, in vogue, may be slowly fading into the sunset. An Unmanned Aircraft System (UAS) by definition includes the airframe, ground control station, command and control links, and crewmembers – all of the equipment necessary for the safe and efficient operation of that aircraft. Organizations such as the Federal Aviation Administration (FAA), the European Aviation Safety Agency (EASA), and the Unmanned Aerial Vehicle Systems Association (UAVSA) favor the use of UAS. The International Civil Aviation Organization (ICAO) is trending use of the term, Remotely Piloted Aircraft System (RPAS).
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning

Learning new skills by observing humans' behaviors is an essential capability of AI. In this work, we leverage instructional videos to study humans' decision-making processes, focusing on learning a model to plan goal-directed actions in real-life videos. In contrast to conventional action recognition, goal-directed actions are based on expectations of their outcomes requiring causal knowledge of potential consequences of actions. Thus, integrating the environment structure with goals is critical for solving this task. Previous works learn a single world model will fail to distinguish various tasks, resulting in an ambiguous latent space; planning through it will gradually neglect the desired outcomes since the global information of the future goal degrades quickly as the procedure evolves. We address these limitations with a new formulation of procedure planning and propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning. Experiments conducted on real-world instructional videos show that our method can achieve state-of-the-art performance in reaching the indicated goals. Furthermore, the learned contextual information presents interesting features for planning in a latent space.
A Comparative Study for Outlier Detection Strategies Based On Traditional Machine Learning For IoT Data Analysis.

Internets of Things (IoT) systems are increasing very fast. They have different types of wireless sensor networks (WSN) behind them. These networks have many applications that are a portion of our life such as healthcare, agricultural, mechanical, and military systems applications. Therefore, a massive amount of data was collected. Outlier detection is one of the essential fundamental problems in these applications. It helps to discover erroneous, imperfect, and noisy nodes. There are various techniques used to detect this outlier. Machine learning algorithm-based approaches are exceptionally much valuable and successful among them. This paper is concerned with the study of outlier detection techniques. It categorizes them into different approaches, such as Statistical, Nearest_Neighbor, Clustering, Subspace, Ensemble-based, and other approaches. These approaches are examined in detail. This study is concerned with determining the best outlier detection method that can be used to detect the outlier in the IoT data analysis. In conclusion, the experimental results show that the Isolation Forest, HBOS, and CBLOF approaches give better performance in terms of precision, Area under the curve (AUC), and execution time than other algorithms.
The Students Willingness and Learning Attitudes Toward Speaking Skill

The purpose of this research is to find out whether or not there was a correlation between the students’ willingness to communicate, and learning attitudes toward speaking skill. This research used a quantitative correlation to find the relationship between the variables. This study’s population consisted of 147 students. The purposive sampling method was used to obtain the sample for this study, which consisted of 25 students from XI IPS 2 and XI IPS 3 classes. The instruments used to obtain data were questionnaires and online test. The questionnaire utilizes a google form with 10 statements and five answers in each statement to assess students’ willingness to communicate, and learning attitudes. The second, test is to obtain the results of each student’s speaking skill, which are recorded. Normality test, homogeneity test, linearity test, bivariate test, multiple correlation equation, and hypothesis test are used to analyze data. Based on data analysis, the multiple regression equation obtained 1.14, implying that there is a very strong relationship between willingness to communicate, and learning attitudes toward speaking skills.
Quantum randomized encoding, verification of quantum computing, no-cloning, and blind quantum computing

Randomized encoding is a powerful cryptographic primitive with various applications such as secure multiparty computation, verifiable computation, parallel cryptography, and complexity lower-bounds. Intuitively, randomized encoding $\hat{f}$ of a function $f$ is another function such that $f(x)$ can be recovered from $\hat{f}(x)$, and nothing except for $f(x)$ is leaked from $\hat{f}(x)$. Its quantum version, quantum randomized encoding, has been introduced recently [Brakerski and Yuen, arXiv:2006.01085]. Intuitively, quantum randomized encoding $\hat{F}$ of a quantum operation $F$ is another quantum operation such that, for any quantum state $\rho$, $F(\rho)$ can be recovered from $\hat{F}(\rho)$, and nothing except for $F(\rho)$ is leaked from $\hat{F}(\rho)$. In this paper, we show that if quantum randomized encoding of BB84 state generations is possible with an encoding operation $E$, then a two-round verification of quantum computing is possible with a classical verifier who can additionally do the operation $E$. One of the most important goals in the field of the verification of quantum computing is to construct a verification protocol with a verifier as classical as possible. This result therefore demonstrates a potential application of quantum randomized encoding to the verification of quantum computing: if we can find a good quantum randomized encoding (in terms of the encoding complexity), then we can construct a good verification protocol of quantum computing. We, however, also show that too good quantum randomized encoding is impossible: if quantum randomized encoding with a classical encoding operation is possible, then the no-cloning is violated. We finally consider a natural modification of blind quantum computing protocols in such a way that the server gets the output like quantum randomized encoding. We show that the modified protocol is not secure.
Channel-wise Knowledge Distillation for Dense Prediction

Knowledge distillation (KD) has been proven to be a simple and effective tool for training compact models. Almost all KD variants for dense prediction tasks align the student and teacher networks' feature maps in the spatial domain, typically by minimizing point-wise and/or pair-wise discrepancy. Observing that in semantic segmentation, some layers' feature activations of each channel tend to encode saliency of scene categories (analogue to class activation mapping), we propose to align features channel-wise between the student and teacher networks. To this end, we first transform the feature map of each channel into a probabilty map using softmax normalization, and then minimize the Kullback-Leibler (KL) divergence of the corresponding channels of the two networks. By doing so, our method focuses on mimicking the soft distributions of channels between networks. In particular, the KL divergence enables learning to pay more attention to the most salient regions of the channel-wise maps, presumably corresponding to the most useful signals for semantic segmentation. Experiments demonstrate that our channel-wise distillation outperforms almost all existing spatial distillation methods for semantic segmentation considerably, and requires less computational cost during training. We consistently achieve superior performance on three benchmarks with various network structures. Code is available at: https://git.io/Distiller