Reviewerly/exHarmony huggingface dataset:
Deep learning for emotion analysis in Arabic tweets

Abstract Currently, expressing feelings through social media requires great consideration as an essential part of our lives; besides sharing ideas and thoughts, we share moments and good memories. Social media such as Facebook, Twitter, Weibo, and LinkedIn, are considered rich sources of opinionated text data. Both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. Most studies on social media analysis mainly classified sentiment as positive, negative, or neutral classes. The challenge in emotion analysis arises because humans can express one or several emotions within one expression. Human beings can recognize these different emotions well; however, it is still not easy for an emotion analysis system. In most cases, the Arabic language used through social media is of a slangy or colloquial form, making it more challenging to preprocess and filter noise since most lemmatization and stemming tools are built on Modern Standard Arabic (MSA). An emotion analysis model has been implemented to categorize emotions. The model is a multiclass and multilabel classification problem. However, few studies have been adapted for this emotion classification problem in Arabic social media. Nearly the only work is the one of SemEval 2018 task1- sub-task E-c. Several machine learning approaches have been implemented in this task; a few studies were based on deep learning. Our model implemented a novel multilayer bidirectional long short term memory (BiLSTM) trained on top of pre-trained word embedding vectors. The model achieved state-of-the-art performance enhancement. This approach has been compared with other models developed in the same tasks using Support Vector Machines (SVM), random forest (RF), and fully connected neural networks. The proposed model achieved a performance improvement over the best results obtained for this task.
Enhancing the Security of Chaotic Direct Sequence Spread Spectrum Communication Through WFRFT

In this letter, the weighted fractional Fourier transform (WFRFT) is performed on the chaotic sequence to enhance the security of the chaotic direct sequence spread spectrum (CD3S) communication, and a WFRFT-CD3S system that spreads the information bits through the WFRFT-chaotic sequence is proposed. In the proposed system, the transmitter scrambles the phase space trajectory of the chaotic sequence through WFRFT and make the distribution of the transmitted signal be Gaussian-like. The receiver synchronizes the WFRFT-chaotic sequence through bidirectional correlation search and demodulates the information through the synchronized local WFRFT-chaotic sequence. Simulations are implemented to evaluate the security performance, synchronization performance, and demodulation performance of the proposed system.
Working Methodology with Public Universities in Peru during the Pandemic—Continuity of Virtual/Online Teaching and Learning

This article describes the consultancy provided by the UTEC-UNED-TECSUP University Consortium to six national universities in Peru, during the COVID-19 state of emergency. This action aims to promote the techno-pedagogical change from a face-to-face to a virtual/online educational context. The process consists of three stages that ensured the continuity of the virtual/online educational service: diagnosis, design, and training, to strengthen instructional and digital competencies, support, and techno-pedagogical monitoring. It includes the basic principles of constructivist and constructionist learning theories for active and quality teaching and learning for the agents involved, and, in addition, the guidelines set by the emerging Peruvian regulations during the pandemic to move towards a digital university model according to the times. After a 157-day intervention, the analysis of the results raises some reflections: the importance of the socio-cultural context and its influence on the concept and development of the instructional act; the concept of distance learning in territories where connectivity is the main difficulty, and the university institution, in terms of a non-presential educational model with open and versatile methodologies, which anticipates a long process, involves a scheduled follow-up, requires fluid communication and demands continuous feedback.
Histopathological Transfer Learning for Acute Lymphoblastic Leukemia Detection

The detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) is being increasingly performed with the help of Computer Aided Diagnosis (CAD) systems based on Deep Learning (DL), that support the pathologists in performing their decision by analyzing the blood samples to determine the presence of lymphoblasts. When using DL, the limited dimensionality of ALL databases favors the use of transfer learning techniques to increase the accuracy in the detection, by considering Convolutional Neural Networks (CNN) pretrained on the general purpose ImageNet database. However, no method in the literature has yet considered the use of CNNs pretrained on histopathology databases to perform transfer learning for ALL detection. In fact, the majority of histopathology databases in the literature has either a small number of samples or limited ground truth labeling possibilities (e.g., only two possible classes), which hinders the effectiveness of training CNNs from scratch. In this paper, we propose the first method based on histopathological transfer learning for ALL detection, that trains a CNN on a histopathology database to classify tissue types, then performs a fine tuning on the ALL database to detect the presence of lymphoblasts. As histopathology database, we consider a multi-label dataset with a significantly higher number of samples and classes with respect to the literature, which enables CNNs to learn general features for histopathology image processing and hence allow to perform a more effective transfer learning, with respect to CNNs pretrained on ImageNet. We evaluate the methodology on a publicly-available ALL database and considering multiple CNNs, with results confirming the validity of our approach.
Filter pruning with a feature map entropy importance criterion for convolution neural networks compressing

Deep Neural Networks (DNN) has made significant progress in recent years. However, its high computing and storage costs make it challenging to apply on resource-limited platforms or edge computation scenarios. Recent studies have shown that model pruning is an effective method to solve this problem. Typically, the model pruning method is a three-stage pipeline: training, pruning, and fine-tuning. In this work, a novel structured pruning method for Convolutional Neural Networks (CNN) compression is proposed, where filter-level redundant weights are pruned according to entropy importance criteria (termed FPEI). In short, the FPEI criterion, which works in the stage of pruning, defines the importance of the filter according to the entropy of feature maps. If a feature map contains very little information, it should not contribute much to the whole network. By removing these uninformative feature maps, their corresponding filters in the current layer and kernels in the next layer can be removed simultaneously. Consequently, the computing and storage costs are significantly reduced. Moreover, because our method cannot show the advantages of the existing ResNet pruning strategy, we propose a dimensionality reduction (DR) pruning strategy for ResNet structured networks. Experiments on several datasets demonstrate that our method is effective. In the experiment about the VGG-16 model on the SVHN dataset, we removed 91.31% of the parameters, from 14.73M to 1.28M, achieving a 63.77% reduction in the FLOPs, from 313.4M to 113.5M, and 1.73 times speedups of model inference.
The Fourth Industrial Revolution in Higher Education

The fourth industrial revolution (4IR) is changing all aspects of our lives. Advancements in artificial intelligence, the internet of things, blockchain and robotics are driving the 4IR. In turn, these developments are changing the very foundation of the modern university. We explore two aspects of the 4IR in this chapter, namely how they affect the academic, as well as the infrastructure (assets) dimension of the university. Regarding the academic dimension, we explore issues such as the curriculum and research. The curriculum dimension includes how to structure curricula and configure degree programs to enable the necessary skills for graduates in terms of the 4IR. The research dimension explores how to enhance research culture within the context of the 4IR, including subjects such as the use of crowdsourcing to improve research performance. Regarding infrastructure aspects, we explore how to structure laboratories, classrooms and other assets using developments in 3D printing, virtual reality and augmented reality. The chapter concludes by studying the economic implications of preparing universities for the 4IR.
Scalable Polyhedral Verification of Recurrent Neural Networks

Abstract We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover , we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.
A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records

Summary Objectives: We survey recent work in biomedical NLP on building more adaptable or generalizable models, with a focus on work dealing with electronic health record (EHR) texts, to better understand recent trends in this area and identify opportunities for future research. Methods: We searched PubMed, the Institute of Electrical and Electronics Engineers (IEEE), the Association for Computational Linguistics (ACL) anthology, the Association for the Advancement of Artificial Intelligence (AAAI) proceedings, and Google Scholar for the years 2018-2020. We reviewed abstracts to identify the most relevant and impactful work, and manually extracted data points from each of these papers to characterize the types of methods and tasks that were studied, in which clinical domains, and current state-of-the-art results. Results: The ubiquity of pre-trained transformers in clinical NLP research has contributed to an increase in domain adaptation and generalization-focused work that uses these models as the key component. Most recently, work has started to train biomedical transformers and to extend the fine-tuning process with additional domain adaptation techniques. We also highlight recent research in cross-lingual adaptation, as a special case of adaptation. Conclusions: While pre-trained transformer models have led to some large performance improvements, general domain pre-training does not always transfer adequately to the clinical domain due to its highly specialized language. There is also much work to be done in showing that the gains obtained by pre-trained transformers are beneficial in real world use cases. The amount of work in domain adaptation and transfer learning is limited by dataset availability and creating datasets for new domains is challenging. The growing body of research in languages other than English is encouraging, and more collaboration between researchers across the language divide would likely accelerate progress in non-English clinical NLP.
Optimal Entanglement Swapping in Quantum Repeaters

We formulate the problem of finding the optimal entanglement swapping scheme in a quantum repeater chain as a Markov decision process and present its solution for different repeaters' sizes. Based on this, we are able to demonstrate that the commonly used ``doubling'' scheme for performing probabilistic entanglement swapping of probabilistically distributed entangled qubit pairs in quantum repeaters does not always produce the best possible raw rate. Focusing on this figure of merit, without considering additional probabilistic elements for error suppression such as entanglement distillation on higher ``nesting levels,'' our approach reveals that a power-of-two number of segments has no privileged position in quantum repeater theory; the best scheme can be constructed for any number of segments. Moreover, classical communication can be included into our scheme, and we show how this influences the raw waiting time for different numbers of segments, confirming again the optimality of ``nondoubling'' in some relevant parameter regimes. Thus, our approach provides the minimal possible waiting time of quantum repeaters in a fairly general physical setting.
Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies

Self-supervised speech representations have been shown to be effective in a variety of speech applications. However, existing representation learning methods generally rely on the autoregressive model and/or observed global dependencies while generating the representation. In this work, we propose Non-Autoregressive Predictive Coding (NPC), a self-supervised method, to learn a speech representation in a non-autoregressive manner by relying only on local dependencies of speech. NPC has a conceptually simple objective and can be implemented easily with the introduced Masked Convolution Blocks. NPC offers a significant speedup for inference since it is parallelizable in time and has a fixed inference time for each time step regardless of the input sequence length. We discuss and verify the effectiveness of NPC by theoretically and empirically comparing it with other methods. We show that the NPC representation is comparable to other methods in speech experiments on phonetic and speaker classification while being more efficient.
Zero-Knowledge Proofs for Set Membership: Efficient, Succinct, Modular

We consider the problem of proving in zero knowledge that an element of a public set satisfies a given property without disclosing the element, i.e., for some u, " $$u \in S$$ and P(u) holds". This problem arises in many applications (anonymous cryptocurrencies, credentials or whitelists) where, for privacy or anonymity reasons, it is crucial to hide certain data while ensuring properties of such data. We design new modular and efficient constructions for this problem through new commit-and-prove zero-knowledge systems for set membership, i.e. schemes proving $$u \in S$$ for a value u that is in a public commitment $$c_u$$ . We also extend our results to support non-membership proofs, i.e. proving $$u \notin S$$ . Being commit-and-prove, our solutions can act as plug-and-play modules in statements of the form " $$u \in S$$ and P(u) holds" by combining our set (non-)membership systems with any other commit-and-prove scheme for P(u). Also, they work with Pedersen commitments over prime order groups which makes them compatible with popular systems such as Bulletproofs or Groth16. We implemented our schemes as a software library, and tested experimentally their performance. Compared to previous work that achieves similar properties—the clever techniques combining zkSNARKs and Merkle Trees in Zcash—our solutions offer more flexibility, shorter public parameters and $$3.7 \times $$ – $$30\times $$ faster proving time for a set of size $$2^{64}$$ .
Defining blockchain governance principles: A comprehensive framework

Blockchain eliminates the need for trusted third-party intermediaries in business by enabling decentralised architecture design in software applications. However, the vulnerabilities in on-chain autonomous decision-makings and cumbersome off-chain coordination lead to serious concerns about blockchain's ability to behave in a trustworthy and efficient way. Blockchain governance has received considerable attention to support the decision-making process during the use and evolution of blockchain. Nevertheless, the conventional governance frameworks do not apply to blockchain due to its distributed architecture and decentralised decision process. These inherent features lead to the absence of a clear source of authority in blockchain ecosystem. Currently, there is a lack of systematic guidance on the governance of blockchain. Therefore, in this paper, we present a comprehensive blockchain governance framework, which elucidates an integrated view of the degree of decentralisation, decision rights, incentives, accountability, ecosystem, and legal and ethical responsibilities. The above aspects are formulated as six high-level principles for blockchain governance. We demonstrate a qualitative analysis of the proposed framework, including case studies on five extant blockchain platforms, and comparison with existing blockchain governance frameworks. The results show that our proposed framework is feasible and applicable in a real-world context.
HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition

Wearable sensor-based human activity recognition (HAR) is the study that deals with sensor data to understand human movement and behavior. In a HAR model, feature extraction is widely considered to be the most essential and challenging part as the sensor signals contain important information in both spatial and temporal contexts. In addition, because people often carry out an activity for awhile before changing to another activity, the sensor data also contain long-term context dependencies. In this paper, in order to enhance the long, short-term and spatial features from the sensor data, we propose a hierarchical deep learning-based HAR model (HiHAR) which is constructed from two powerful deep neural network architectures: convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM).With the hierarchical structure, HiHAR contains two stages: local and global. In the local stage, a CNN and a BiLSTM are applied on the window-data level to extract local spatiotemporal features. The global stage with another BiSLTM is used to extract long-term context information from adjacent windows in both forward and backward time directions, then performs activity classification task. Our experiment results on two public datasets (UCI HAPT and MobiAct scenario) indicate that the proposed hybrid model achieves competitive performance compared to other state-of-the-art HAR models with an average accuracy of 97.98% and 96.16%, respectively.
On the Hardness of the NTRU Problem

The 25 year-old NTRU problem is an important computational assumption in public-key cryptography. However, from a reduction perspective, its relative hardness compared to other problems on Euclidean lattices is not well-understood. Its decision version reduces to the search Ring-LWE problem, but this only provides a hardness upper bound.We provide two answers to the long-standing open problem of providing reduction-based evidence of the hardness of the NTRU problem. First, we reduce the worst-case approximate Shortest Vector Problem over ideal lattices to an average-case search variant of the NTRU problem. Second, we reduce another average-case search variant of the NTRU problem to the decision NTRU problem.
A semi-supervised 3D object detection method for autonomous driving

3D object detection is very important for autonomous driving, but most of algorithms heavily rely on data annotation, which is a laborious job. Semi-supervised learning is an effective strategy to reduce the heavy annotation burden in this task. In this paper, we propose a semi-supervised 3D object detection method. To reduce the workload of 3D annotations, we adopt the teacher-student framework to generate pseudo-labels from unlabeled training data, and use a label filtering method to improve the pseudo label quality. For the convenience of deployment, we introduce the simple and fast one-stage object detector. We validate our method on the KITTI dataset. For moderate Car detection task, our method can achieve 76.28 mAP using half labels compared with 77.34 mAP of the PointPillars using all labels. Experiment results show that our method can also achieve approximate performance on Pedestrian and Cyclist detection task with fewer labels.
Stabilizing a supervised bot detection algorithm: How much data is needed for consistent predictions?

Social media bots have been characterized in their use in digital activism and information manipulation, due to their roles in information diffusion. The detection of bots has been a major task within the field of social media computation, and many datasets and bot detection algorithms have been developed. With these algorithms, the bot score stability is key in estimating the impact of bots on the diffusion of information. Within several experiments on Twitter agents, we quantify the amount of data required for consistent bot predictions and analyze agent bot classification behavior. Through this study, we developed a methodology to establish parameters for stabilizing the bot probability score through threshold, temporal and volume analysis, eventually quantifying suitable threshold values for bot classification (i.e. whether the agent is a bot or not) and reasonable data collection size (i.e. number of days of tweets or number of tweets) for stable scores and bot classification.
A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing

Cloud computing is a technology in which the resources are delivered as services. Users can access them anywhere, anytime via the Internet without any need to know the infrastructure knowledge, experience, or even authority that provides such services. It has become an important medium for enterprises to develop their networks because of these resources. With the growing need for cloud computing, security is becoming important for both individuals and business needs. Many researchers have studied the security of cloud computing. Still, security gaps or threats are increasing as the demand for cloud computing connectivity increases. Because a third party mostly provides cloud platforms to the cloud client, data protection in the cloud is the primary issue. This paper presents a novel, effective lightweight homomorphic cryptographic algorithm which contains two layers of encryption. The first layer uses the new effective, light-weight cryptographic algorithm and the second layer multiplicative homomorphic schemes considered for improving security data in cloud computing. This approach offers both symmetric and asymmetric cryptography features. The proposed approach's performance is evaluated using a variety of metrics, including memory, computational time and (key sensitivity), statistical analysis, image histogram, and entropy change analysis. The proposed algorithm's experimental findings showed a high level of security and an apparent improvement in encryption execution time, memory usage, and throughput. When compared to the cryptographic systems widely used in cloud computing.