Reviewerly/exHarmony huggingface dataset:
Deep Learning for Video Classification: A Review

&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Video classification task has gained a significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition to the importance of video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are a number of existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers are either outdated, and therefore, do not include the recent state-of-art works or they have some limitations. In order to provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the data sets used. To make the review self- contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided, and the critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Building Capacity For Digital Development In The Arab World: The Role Of Education

The digital economy is identified with the 4th industrial revolution reflecting the importance of the digital technologies for development. In particular, the 4th industrial revolution holds prospect for developing countries to benefit from new opportunities brought by the new digital technologies spurring knowledge creation, innovation, skill intensification of the work-force and productivity enhancement. Economic development is a process of structural transformation that requires knowledge creation, knowledge sharing and knowledge acquisition. Digital technologies facilitate access to global markets allowing developing countries taking advantage of the information age to strengthen knowledge creation, innovation diffusion and information dissemination. Education plays critical role in building capacity for development in the digital economy. Participation in the digital economy requires new managerial and technical skills to stimulate technological learning and facilitate cooperation and collaboration. Education increases the country stock of capital essential for building capacity for innovation and supporting sustainable development. In other words, education drives knowledge creation, technological breakthrough and new product development. This paper focuses on the role that education plays in strengthening the fundamentals for digital economy. In most Arab countries, development is factor-driven -- dependent largely on natural resources and labor. This paper examines the contribution of education to development in the era of the 4th industrial revolution.
Encryption Technique to Optimize Information Leakage in Multi Cloud Storage Services

Multicloud is one such aspect which is considered as the vision of the future. It includes distribution of data for the user automatically by means of cloud storage providers (CSPs) which in turn provides a considerable degree of control over information leakage. Due to the process of unplanned distribution of data chunks will result in disposing the crucial information while using multiple clouds. A storage system named Storesim is introduced in Multicloud to store syntactically relevant data on the assigned cloud, thereby limiting the intensity of leakage of information across those clouds. During this survey it has been identified that few CSP’s of some Multicloud services got compromised under coercion. In this paper, we made use of the Ultramodern Encryption Standard (UES) into storesim thereby avoiding unauthorized access to the information present in the Multicloud ensuring better level of security to CSP’s implicating reduced information leakage.
Signature Verification using Convolutional Neural Network and Autoencoder

Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature verification. Random distortions were generated in genuine images using an autoencoder to get forged signatures, which were passed to the classifier during training. The paper details all the pre-processing steps carried out on the image and shows various test results for changing the number of training sets of images. The average test accuracy for Persian dataset is 83% when the system was trained with 22 genuine images. There was a decrease of 9.4% in accuracy when the model was trained with 9 genuine images.
IndoNLI: A Natural Language Inference Dataset for Indonesian

We present IndoNLI, the first human-elicited NLI dataset for Indonesian.We adapt the data collection protocol for MNLI and collect ∼18K sentence pairs annotated by crowd workers and experts.The expert-annotated data is used exclusively as a test set.It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.Experiment results show that XLM-R outperforms other pretrained models in our data.The best performance on the expert-annotated data is still far below human performance (13.4% accuracy gap), suggesting that this test set is especially challenging.Furthermore, our analysis shows that our expert-annotated data is more diverse and contains fewer annotation artifacts than the crowd-annotated data.We hope this dataset can help accelerate progress in Indonesian NLP research.
Recent Advancements of Augmented Reality in Real Time Applications

Augmented Reality (AR) is an advanced technology that improves users view of interactivity with the present reality by adding virtual objects in it and it is the most natural way to interface with your digital world. This paper presents a survey about real-time applications, commonly used technologies, various open challenges, possible set of solution provided by several researchers and academicians. This paper also provides future of Augmented reality in various areas of artificial intelligence.
Improving Transparency, Falsifiability, and Rigour by Making Hypothesis Tests Machine Readable

Making scientific information machine-readable greatly facilitates its re-use. Many scientific articles have the goal to test a hypothesis, so making the tests of statistical predictions easier to find and access could be very beneficial. We propose an approach that can be used to make hypothesis tests machine readable. We believe there are two benefits to specifying a hypothesis test in a way that a computer can evaluate whether the statistical prediction is corroborated or not. First, hypothesis test will become more transparent, falsifiable, and rigorous. Second, scientists will benefit if information related to hypothesis tests in scientific articles is easily findable and re-usable, for example when performing meta-analyses, during peer review, and when examining meta-scientific research questions. We examine what a machine readable hypothesis test should look like, and demonstrate the feasibility of machine readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.
Breakdown of the Weak-Coupling Limit in Quantum Annealing

Reverse annealing is a variant of quantum annealing, in which the system is prepared in a classical state, reverse-annealed to an inversion point, and then forward-annealed. We report on reverse annealing experiments using the D-Wave 2000Q device, with a focus on the $p=2$ $p$-spin problem, which undergoes a second order quantum phase transition with a gap that closes polynomially in the number of spins. We concentrate on the total and partial success probabilities, the latter being the probabilities of finding each of two degenerate ground states of all spins up or all spins down, the former being their sum. The empirical partial success probabilities exhibit a strong asymmetry between the two degenerate ground states, depending on the initial state of the reverse anneal. To explain these results, we perform open-system simulations using master equations in the limits of weak and strong coupling to the bath. The former, known as the adiabatic master equation (AME), with decoherence in the instantaneous energy eigenbasis, predicts perfect symmetry between the two degenerate ground states, thus failing to agree with the experiment. In contrast, the latter, known as the polaron transformed Redfield equation (PTRE), is in close agreement with experiment. Thus our results present a challenge to the sufficiency of the weak system-bath coupling limit in describing the dynamics of current experimental quantum annealers, at least for reverse annealing on timescales of a microsecond or longer.
Toward Quantum Computing with Molecular Electronics

In this study, we explore the use of molecules and molecular electronics for quantum computing. We construct one-qubit gates using one-electron scattering in molecules and two-qubit controlled-phase gates using electron-electron scattering along metallic leads. Furthermore, we propose a class of circuit implementations, and show initial applications of the framework by illustrating one-qubit gates using the molecular electronic structure of molecular hydrogen as a baseline model.
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant challenges. To overcome this, various compression techniques have been widely employed to optimize DNN accelerators. A promising approach is quantization, in which the full-precision values are stored in low bit-width precision. Quantization not only reduces memory requirements but also replaces high-cost operations with low-cost ones. DNN quantization offers flexibility and efficiency in hardware design, making it a widely adopted technique in various methods. Since quantization has been extensively utilized in previous works, there is a need for an integrated report that provides an understanding, analysis, and comparison of different quantization approaches. Consequently, we present a comprehensive survey of quantization concepts and methods, with a focus on image classification. We describe clustering-based quantization methods and explore the use of a scale factor parameter for approximating full-precision values. Moreover, we thoroughly review the training of a quantized DNN, including the use of a straight-through estimator and quantization regularization. We explain the replacement of floating-point operations with low-cost bitwise operations in a quantized DNN and the sensitivity of different layers in quantization. Furthermore, we highlight the evaluation metrics for quantization methods and important benchmarks in the image classification task. We also present the accuracy of the state-of-the-art methods on CIFAR-10 and ImageNet.
A QR code-based user-friendly visual cryptography scheme

Benefiting from the development of the Internet and smart devices, it is now convenient to transmit images anywhere and anytime, which poses a new challenge for image security. The Visual Cryptography Scheme (VCS) is a secret sharing method for protecting an image without a key, the merit of VCS is the human visual system (HVS) can restore the secret image by simply superimposing qualified shares, without any computation. To eliminate noise-like shares in traditional VCS, this paper presents a novel QR code-based expansion-free and meaningful visual cryptography scheme (QEVCS), which generates visually appealing QR codes for transmitting meaningful shares. When distributing on public networks, this scheme does not attract the attention of potential attackers. By limiting the gray-level of a halftoned image, QEVCS both keep the computation-free of visual cryptography and the size of recovery image same as the secret images. The experimental results show the effectiveness of QEVCS when preserving the privacy of images.
UI / UX Design Model for Student Complaint Handling Application Using Design Thinking Method (Case Study: STMIK Rosma Karawang)

The development of information technology is currently very rapid and unavoidable, especially in the world of education. Global demands require the education sector to constantly adjust technological developments with efforts to improve the quality of education, adjust the use of information and communication technology. STMIK Rosma is a university that utilizes computer technology. However, there are still shortcomings, namely the absence of a student complaint application so that the process of submitting student complaints is still running manually. This results in not recording student complaints, student complaint data not well documented, so that the college leadership of the campus cannot monitor the complaints submitted by students. This study aims to analyze the functional requirements of the STMIK Rosma student complaint application. The method used in this research is Design Thinking. The result of this research is the UI / UX design of the student complaint handling application (mobile application) at STMIK Rosma Karawang which is designed using the classic mockuplus tools. This research is expected to provide suggestions to universities to improve the process of handling student complaints.
Two ways to make your robot proactive: Reasoning about human intentions or reasoning about possible futures

Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.
Towards Improved Zero-shot Voice Conversion with Conditional DSVAE

Disentangling content and speaking style information is essential for zero-shot non-parallel voice conversion (VC). Our previous study investigated a novel framework with disentangled sequential variational autoencoder (DSVAE) as the backbone for information decomposition. We have demonstrated that simultaneous disentangling content embedding and speaker embedding from one utterance is feasible for zero-shot VC. In this study, we continue the direction by raising one concern about the prior distribution of content branch in the DSVAE baseline. We find the random initialized prior distribution will force the content embedding to reduce the phonetic-structure information during the learning process, which is not a desired property. Here, we seek to achieve a better content embedding with more phonetic information preserved. We propose conditional DSVAE, a new model that enables content bias as a condition to the prior modeling and reshapes the content embedding sampled from the posterior distribution. In our experiment on the VCTK dataset, we demonstrate that content embeddings derived from the conditional DSVAE overcome the randomness and achieve a much better phoneme classification accuracy, a stabilized vocalization and a better zero-shot VC performance compared with the competitive DSVAE baseline.
A Transparency Index Framework for AI in Education

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.
Assessing the viability of an Indian Central Bank Digital Currency (CBDC)

&#x0D; A large number of Central Banks around the world are planning to introduce Central Bank Digital Currencies (CBDCs) as a legal tender in their countries. The Reserve Bank of India (RBI) has also revealed similar plans, with an Indian CBDC expected in the near future. Any evaluation of such a major change in the nature of money requires a broader understanding of the opportunities and challenges arising from the adoption of CBDCs. In this paper, we discuss these issues at the conceptual level and specifically in the Indian context. We show that the conceptual issues can be characterised in three ways – monetary sovereignty issues, issues from the point of view of national sovereignty, and developmental issues. In the Indian context, we analyse these issues from the perspective of the rapid digitalization taking place in the country. Finally, we discuss the steps that the RBI needs to take in order to introduce an Indian CBDC.&#x0D;
Evolutionary Game Analysis on Cloud Providers and Enterprises’ Strategies for Migrating to Cloud-Native under Digital Transformation

Cloud-native is an innovative technology and methodology that is necessary to realize the digital transformation of enterprises. Promoting the wide adoption of cloud-native in cloud providers and enterprises has gained popularity in recent years. According to the technological and commercial characteristics of cloud-native, this paper analyzes the game relationship between cloud providers and enterprises on the selection of cloud-native, and combines evolutionary game theory to establish a model. In addition, empirical analysis indicates the impact of parameter changes on the dynamic evolution process. The results show that (1) enterprises are more vulnerable to the impact of direct benefit to adopt cloud-native, and cloud providers are especially affected by the cost of providing cloud-native; (2) enterprises are more likely to be impacted by the invisible benefit than cloud providers, but the impact has a marginal decreasing effect; (3) the low price is one of the reasons to attract enterprises; (4) enterprises are more concerned about the potential loss caused by the supply and demand mismatch. The results of the discussion provide a reference for all stakeholders to promote the implementation of cloud-native and the digital transformation of enterprises.
BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation

The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2) systematic noises in depth images. Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented from RGB-D images by locally matching pairs of oriented points between the model and camera space. To this end, we propose a novel Bi-directional Correspondence Mapping Network (BiCo-Net) to first generate point clouds guided by a typical pose regression, which can thus incorporate pose-sensitive information to optimize generation of local coordinates and their normal vectors. As pose predictions via geometric computation only rely on one single pair of local oriented points, our BiCo-Net can achieve robustness against sparse and occluded point clouds. An ensemble of redundant pose predictions from locally matching and direct pose regression further refines final pose output against noisy observations. Experimental results on three popularly benchmarking datasets can verify that our method can achieve state-of-the-art performance, especially for the more challenging severe occluded scenes. Source codes are available at https://github.com/Gorilla-Lab-SCUT/BiCo-Net.