Reviewerly/exHarmony huggingface dataset:
Analysis of Witnesses in the Steem Blockchain

Abstract Online Social Networking platforms (OSNs) have become part of people’s everyday life and their usage covers the deep-rooted need for communication among humans. During recent years, as people are questioning more and more OSN service providers, a new generation of proposals, based on blockchain became very popular thanks to the ethics adopted by these platforms. Steemit is the most important blockchain-based social networking site, which integrates, as main novelty an economic layer to the social media service. Steemit is implemented on top of Steem which, as in other blockchains, awards miners of the blocks with cryptocurrency. Steem miners, called witnesses, are not chosen based on the solution of a mathematical problem, as in Proof of Work based systems, but must be voted by other users. In this work, we decide to study the witnesses on Steem and their contribution to the social platform Steemit, and their social impact. We performed a set of analyses to shred light concerning their behaviour and to understand how they are socially perceived by other users. Analyses show an important social impact but, at the same time, some of them have a negative social impact. Their discussion is polarized towards content concerning Steem, Steemit, witnesses, and other platforms hosted on Steem.
HPC Data Center Participation in Demand Response: An Adaptive Policy With QoS Assurance

Demand response programs help stabilize the electricity grid by providing monetary stimulus to consumers if they regulate their power consumption following market requirements. Regulation service, a market that requires participants to regulate power by following a signal updated every few seconds, is particularly beneficial to HPC data centers since data centers are capable of increasing/decreasing power consumption owing to the flexibility in running workloads and the availability of power control mechanisms. While prior works have explored how data centers can provide regulation service reserves, Quality-of-Service (QoS) provisioning for the jobs running at the data centers has not been considered. In this work, we propose an Adaptive policy with QoS Assurance that enables data centers to participate in regulation service programs with assurance on job QoS. Our policy regulates data center power through job scheduling and server power capping. QoS assurance is achieved by applying a queueing-theoretic result to our job scheduling strategy. We evaluate our policy by experiments on a real cluster. Our results demonstrate that the proposed policy reduces electricity costs by 25-56 percent while providing QoS assurance. On the other hand, the baseline policies cannot meet QoS constraints in 9 of the 14 workload traces tested.
Graph Decoupling Attention Markov Networks for Semisupervised Graph Node Classification

Graph neural networks (GNNs) have been ubiquitous in graph node classification tasks. Most GNN methods update the node embedding iteratively by aggregating its neighbors' information. However, they often suffer from negative disturbances, due to edges connecting nodes with different labels. One approach to alleviate this negative disturbance is to use attention to learn the weights of aggregation, but current attention-based GNNs only consider feature similarity and suffer from the lack of supervision. In this article, we consider label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention. The hard attention is learned on labels for a refined graph structure with fewer interclass edges so that the aggregation's negative disturbance can be reduced. The soft attention aims to learn the aggregation weights based on features over the refined graph structure to enhance information gains during message passing. Particularly, we formulate our model under the expectation-maximization (EM) framework, and the learned attention is used to guide label propagation in the M-step and feature propagation in the E-step, respectively. Extensive experiments are performed on six well-known benchmark graph datasets to verify the effectiveness of the proposed method.
Quantum Computing: An Entanglement Measurement

Quantum Computing technologies with rapid proliferation have achieved important milestones in recent years. Quantum concept of entanglement has emerged rapidly with its application domains widely expanding. Here, in this study we elaborate on relationship of classical and quantum technologies focusing on the entanglement property. This study reviews how entanglement influences various quantum applications and elaborates on qubit behaviour to compare entanglement realization on real quantum hardware prototypes and simulated noise model environments in IBM Q Experience which could assist future studies in overcoming associated challenges in entanglement utility in practical implementations of upcoming applications.
Mispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling

A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciation of a native speaker. This approach makes two simplifying assumptions: a) phonemes can be recognized from speech with high accuracy, b) there is a single correct way for a sentence to be pronounced. These assumptions do not always hold, which can result in a significant amount of false mispronunciation alarms. We propose a novel approach to overcome this problem based on two principles: a) taking into account uncertainty in the automatic phoneme recognition step, b) accounting for the fact that there may be multiple valid pronunciations. We evaluate the model on non-native (L2) English speech of German, Italian and Polish speakers, where it is shown to increase the precision of detecting mispronunciations by up to 18% (relative) compared to the common approach.
Lagrangian Decomposition for Neural Network Verification.

A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. Previous methods have either used off-the-shelf solvers, discarding the problem structure, or relaxed the problem even further, making the bounds unnecessarily loose. We propose a novel approach based on Lagrangian Decomposition. Our formulation admits an efficient supergradient ascent algorithm, as well as an improved proximal algorithm. Both the algorithms offer three advantages: (i) they yield bounds that are provably at least as tight as previous dual algorithms relying on Lagrangian relaxations; (ii) they are based on operations analogous to forward/backward pass of neural networks layers and are therefore easily parallelizable, amenable to GPU implementation and able to take advantage of the convolutional structure of problems; and (iii) they allow for anytime stopping while still providing valid bounds. Empirically, we show that we obtain bounds comparable with off-the-shelf solvers in a fraction of their running time, and obtain tighter bounds in the same time as previous dual algorithms. This results in an overall speed-up when employing the bounds for formal verification. Code for our algorithms is available at https://github.com/oval-group/decomposition-plnn-bounds.
A Multi-Criteria Optimal Operation Framework for Renewable Energy Integrated Data Center Microgrid with Waste Heat Recovery

The rapid development of data centers (DCs) leads to a huge challenge in their energy consumption and environmental impact. It is promising to establish data center microgrid (DCMG) with renewable energy generation and waste heat recovery system to address these issues. However, the efficient energy management of DCMG is to be pursued. In this paper, we propose a multi-criteria optimal operation framework for the DCMG, considering coordinated scheduling of energy supply and demand. In this framework, multiple criteria are comprehensively considered via a multi-criteria optimization (MCO) approach. Then, we adopt the augmented e-constraint algorithm to address the corresponding MCO problem. Afterwards, a balanced decision making method is further proposed to determine the optimal scheduling solution. Finally, case study and results analysis verify the effectiveness of proposed multi-criteria optimal operation fr-amework for the DCMG.
GPT‐3: Its Nature, Scope, Limits, and Consequences

In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic (that is, the Turing Test), and ethical questions and show that GPT-3 is not designed to pass any of them. This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts.
Constructing a Shared Infrastructure for Software Architecture Analysis and Maintenance

Over the past three decades software engineering researchers have produced a wide range of techniques and tools for understanding the architectures of large, complex systems. However, these have tended to be one-off research projects, and their idiosyncratic natures have hampered research collaboration, extension and combination of the tools, and technology transfer. The area of software architecture is rich with disjoint research and development infrastructures, and datasets that are either proprietary or captured in proprietary formats. This paper describes a concerted effort to reverse these trends. We have designed and implemented a flexible and extensible infrastructure (SAIN) with the goal of sharing, replicating, and advancing software architecture research. We have demonstrated that SAIN is capable of incorporating the constituent tools extracted from three independently developed, large, long-lived software architecture research environments. We discuss SAIN's ambitious goals, the challenges we have faced in achieving those goals, the key decisions made in SAIN's design and implementation, the lessons learned from our experience to date, and our ongoing and future work.
Targeting the Weakest Link: Social Engineering Attacks in Ethereum Smart Contracts

Ethereum holds multiple billions of U.S. dollars in the form of Ether cryptocurrency and ERC-20 tokens, with millions of deployed smart contracts algorithmically operating these funds. Unsurprisingly, the security of Ethereum smart contracts has been under rigorous scrutiny. In recent years, numerous defense tools have been developed to detect different types of smart contract code vulnerabilities. When opportunities for exploiting code vulnerabilities diminish, the attackers start resorting to social engineering attacks, which aim to influence humans - often the weakest link in the system. The only known class of social engineering attacks in Ethereum are honeypots, which plant hidden traps for attackers attempting to exploit existing vulnerabilities, thereby targeting only a small population of potential victims.
Augmented Reality in Science Education: An Application in Higher Education

Effective usage of augmented reality(AR) depends on good integration into the learning environment. Based on a qualitative research approach, this study investigates the effects of using AR technologies in science education to support the effective use of AR. Students’ experiences of AR were gathered using a prepared questionnaire form. Within the scope of science education, AR was used in a university-level chemistry course. Using theme analysis, descriptive themes were created by analyzing the content of completed questionnaires in written texts. Descriptive expressions obtained from the written text were determined by free coding. These codes were then matched with appropriate themes and illustrated in the form of branched trees. The study results demonstrated that AR is an optimal tool for teaching abstract subjects that do not feature direct observation and examination in science education. Students also have positive opinions about the use of AR in other courses in science education. Another important result from this study revealed that AR software interfaces require improvements to be suitable as teaching material. In addition, several recommendations have been presented for the better integration of AR into the learning environment.
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference

Meta-learning promises few-shot learners that quickly adapt to new distributions by repurposing knowledge acquired from previous training.However, we believe meta-learning has not yet succeeded in NLP due to the lack of a well-defined task distribution, leading to attempts that treat datasets as tasks.Such an ad hoc task distribution causes problems of quantity and quality.Since there's only a handful of datasets for any NLP problem, meta-learners tend to overfit their adaptation mechanism and, since NLP datasets are highly heterogeneous, many learning episodes have poor transfer between their support and query sets, which discourages the meta-learner from adapting.To alleviate these issues, we propose DRECA (Decomposing datasets into Reasoning Categories), a simple method for discovering and using latent reasoning categories in a dataset, to form additional high quality tasks.DRECA works by splitting examples into label groups, embedding them with a finetuned BERT model and then clustering each group into reasoning categories.Across four few-shot NLI problems, we demonstrate that using DRECA improves the accuracy of meta-learners by 1.5-4%.
Degeneracy and Its Impact on the Decoding of Sparse Quantum Codes

The well-documented capacity-approaching performance of sparse codes in the realm of classical communications has inspired the search for their quantum counterparts. Sparse quantum codes are generally built as the amalgamation of two robust classical codes and are decoded via classical decoding algorithms. However, the quantum paradigm presents phenomena that act in a deleterious manner on sparse quantum codes when they are decoded based on classical methodologies. One such phenomenon is known as degeneracy, and it is a major contributor to why sparse quantum codes do not entirely evoke the stupendous error correcting abilities of their classical counterparts. In this paper, we adopt a group theoretical approach to discuss the issue of degeneracy as it relates to sparse quantum codes. Furthermore, we compare the decoding process of sparse quantum codes with that of sparse classical codes, illustrating the challenges that appear in the quantum domain. Finally, we provide a detailed example to illustrate the effects of degeneracy on sparse quantum codes and the challenges of designing an optimum decoder for these schemes.
Rocca: An Efficient AES-based Encryption Scheme for Beyond 5G

In this paper, we present an AES-based authenticated-encryption with associated-data scheme called Rocca, with the purpose to reach the requirements on the speed and security in 6G systems. To achieve ultra-fast software implementations, the basic design strategy is to take full advantage of the AES-NI and SIMD instructions as that of the AEGIS family and Tiaoxin-346. Although Jean and Nikolić have generalized the way to construct efficient round functions using only one round of AES (aesenc) and 128-bit XOR operation and have found several efficient candidates, there still seems to exist potential to further improve it regarding speed and state size. In order to minimize the critical path of one round, we remove the case of applying both aesenc and XOR in a cascade way for one round. By introducing a cost-free block permutation in the round function, we are able to search for candidates in a larger space without sacrificing the performance. Consequently, we obtain more efficient constructions with a smaller state size than candidates by Jean and Nikolić. Based on the newly-discovered round function, we carefully design the corresponding AEAD scheme with 256-bit security by taking several reported attacks on the AEGIS family and Tiaxion-346 into account. Our AEAD scheme can reach 138Gbps which is 4 times faster than the AEAD scheme of SNOW-V. Rocca is also much faster than other efficient schemes with 256-bit key length, e.g. AEGIS-256 and AES-256-GCM. As far as we know, Rocca is the first dedicated cryptographic algorithm targeting 6 systems, i.e., 256-bit key length and the speed of more than 100 Gbps.
Self-supervised deep metric learning for ancient papyrus fragments retrieval

This work focuses on document fragments association using deep metric learning methods. More precisely, we are interested in ancient papyri fragments that need to be reconstructed prior to their analysis by papyrologists. This is a challenging task to automatize using machine learning algorithms because labeled data is rare, often incomplete, imbalanced and of inconsistent conservation states. However, there is a real need for such software in the papyrology community as the process of reconstructing the papyri by hand is extremely time-consuming and tedious. In this paper, we explore ways in which papyrologists can obtain useful matching suggestion on new data using Deep Convolutional Siamese-Networks. We emphasize on low-to-no human intervention for annotating images. We show that the from-scratch self-supervised approach we propose is more effective than using knowledge transfer from a large dataset, the former achieving a top-1 accuracy score of 0.73 on a retrieval task involving 800 fragments.
WeatherEye-Proposal of an Algorithm Able to Classify Weather Conditions from Traffic Camera Images

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new artificial intelligence method that uses road traffic cameras and a convolution neural network to detect weather conditions has, therefore, been proposed. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and order them according to their intensity. The method can differentiate between five weather conditions such as normal (no precipitation), heavy rain, light rain, heavy fog and light fog. The deep-learning method’s training and testing phases were conducted using a new database called the Cerema-AWH (Adverse Weather Highway) database. After several optimisation steps, the proposed method obtained an accuracy of 0.99 for classification.
Blockchain Encryption on Student Academic Transcripts using a Smart Contract

As an innovation in the world of computers, blockchain has many benefits and is also widely applied in the world of education. Blockchain itself has many advantages, especially in the world of education. Blockchain is a digital data storage system that consists of many servers (multiserver). In this Blockchain technology, data created by one server can be replicated and verified by another server. By using this technology with a decentralized system and strong cryptography and can help colleges or universities to build infrastructure in the archive storage of transcripts, diplomas, and diplomas. Usage One of the blockchain technology applications in education is iBC, namely the e-learning Blockchain Certificate, book copyright, and also e-Portfolios. iBC or e-learning Blockchain Certificate is a tool designed to create, verify and also issue blockchain certificates. As has been supported by the IBC to create certificates that are globally verified and stored in a decentralized manner. Here will be presented use cases that are relevant in the use of Blockchain technology in educational environments, especially data processing in universities and we also try to design an IBC based on blockchain technology that can be used to support transparency and accountability of colleges or universities in issuing diplomas and grades.Â
Multimodal Summarization of User-Generated Videos

The exponential growth of user-generated content has increased the need for efficient video summarization schemes. However, most approaches underestimate the power of aural features, while they are designed to work mainly on commercial/professional videos. In this work, we present an approach that uses both aural and visual features in order to create video summaries from user-generated videos. Our approach produces dynamic video summaries, that is, comprising the most “important” parts of the original video, which are arranged so as to preserve their temporal order. We use supervised knowledge from both the aforementioned modalities and train a binary classifier, which learns to recognize the important parts of videos. Moreover, we present a novel user-generated dataset which contains videos from several categories. Every 1 s part of each video from our dataset has been annotated by more than three annotators as being important or not. We evaluate our approach using several classification strategies based on audio, video and fused features. Our experimental results illustrate the potential of our approach.
OCFormer: A Transformer-Based Model For Arabic Handwritten Text Recognition

The Optical Character Recognition (OCR) of Arabic historical documents is a challenging task. The reason being the complexity of the layout and the highly variant typography. Nonetheless, in recent years, with the rise of Deep learning, significant progress has been made in historical OCR; in both layout recognition and segmentation, and also in character recognition. The only downside is the limited advancements dedicated to the Arabic language, notably the handwritten text. In this paper, we present an OCR approach that utilizes state-of-theart Deep learning techniques for the Arabic language. We built a custom dataset of obfuscated and noisy images to imitate the noise in historical Arabic documents, with a collection of 30 million images paired with their ground truth. The model utilizes both page segmentation and line segmentation techniques to enhance the resultant transcription. The model is complex enough for transcribing handwritten manuscripts. In addition, the model can detect and transcribe documents that contain Arabic diacritics. The model attained a CER of 0.0727, a WER of 0.0829, and a SER of 0.10.