Reviewerly/exHarmony huggingface dataset:
Variance of the sum of independent quantum computing errors

The sum of quantum computing errors is the key element both for the estimation and control of errors in quantum computing and for its statistical study. In this article we analyze the sum of two independent quantum computing errors, X_1 and X_2, and we obtain the formula of the variance of the sum of these errors: V(X_1+X_2)=V(X_1)+V(X_2)-\frac{V(X_1)V(X_2)}{2}. We conjecture that this result holds true for general quantum computing errors and we prove the formula for independent isotropic quantum computing errors.
An intervention study to prevent ‘summer reading loss’ in a socioeconomically disadvantaged area with second language learners

Summer reading loss is a documented reality for many students. Research has established differences in the contribution of summer reading activity between children from families with different economic status. In this study, 120 students in Grade 2 and 115 students in Grade 3 from a socioeconomically vulnerable area participated in a summer reading intervention. In addition, a control group from the same schools comprised of 106 students from Grade 2 and 94 students from Grade 3. Almost 90% of the participating students did not have Swedish as their native language. The participants were tested on reading skills, including word decoding, nonsense-word reading, word comprehension and reading comprehension, before and after the summer vacation. The intervention was planned together with teachers from three participant schools and leisure centers. Before the summer holiday the schools arranged reading weeks and library visits. The students were encouraged to read at home during the vacation and record the number of books read on a digital platform. The results showed that the largest effect sizes between groups (intervention and control) were observed for word decoding in Grade 2 and word comprehension in Grade 3 where the intervention group improved more than the control group. If summer learning loss can be avoided or limited, the treatment can be considered worth implementing.
Visualisasi Bibliometrik Penelitian Bidang Ilmu Kegempaan di Indonesia Berbasis Data Scopus Tahun 1988-2018

Penelitian ini bertujuan untuk melihat; 1) pertumbuhan publikasi ilmiah tentang gempa di Indonesia, 2) jurnal inti publikasi ilmiah ilmu tentang gempa di Indonesia, 3) produktivitas dan kolaborasi peneliti ilmu tentang gempa, 4) jumlah publikasi ilmu kegempaan berdasarkan afiliasi/lembaga, 5) jumlah publikasi berdasarkan negara, 6) jumlah publikasi berdasarkan tipe dokumen, 7) jumlah publikasi berdasarkan subjek, 8) peta perkembangan tentang gempa melalui kluster kata kunci. Data diperolah penelitian melalui database Scopus yang terbit pada periode tahun 1988-2018. Analisis data menggunakan software VOSviewer. Hasil penelitian menunjukkan bahwa Geophysical Research Letters, Journal of Volcanology and Geothermal Research, IOP Conference Series Earth and Environmental Science, dan Geophysical Journal International banyak mempublikasikan kajian tentang gempa. Subjek yang terbanyak diteliti adalah Earth and Planetary Sciences, Engineering, Energy, Environmental Science, dan Physics and Astronomy. Tren penelitian terbanyak masalah seismology, seismic wave, seismic data, geophysics dan mathematical model, Indonesia, Asia, Eurasia, tectonics dan eartquake rupture, volcanoes dan tremor.ABSTRACT:This study aims to describe; 1) growth of scientific publications on earthquakes in Indonesia, 2) core journal of scientific publications on earthquake sciences in Indonesia, 3) productivity and collaboration of earthquake researchers, 4) number of seismic science publications based on affiliation/institutions, 5) number of publications by country, 6) number of publications based on document type, 7) number of publications based on subjects, 8) map of developments on earthquakes through keyword clustering based on VOSviewer software. The data were gathered through the Scopus database from the period of time 1988-2018. The data was analyzed by using VOSviewer. The study found that most earthquakes publication was published in Geophysical Research Letters, Journal of Volcanology and Geothermal Research, IOP Conference Series Earth and Environmental Science, and Geophysical Journal International. The most studied subjects were Earth and Planetary Sciences, then followed by Engineering, Energy, Environmental Science, and Physics and Astronomy. From bibliometric visualization shows that the most research trends are problems of seismology, seismic wave, seismic data, geophysics, and mathematical models, Indonesia, Asia, Eurasia, tectonics and earthquake rupture, volcanoes and tremors.
Adaptive Two-Dimensional Embedded Image Clustering

With the rapid development of mobile devices, people are generating huge volumes of images data every day for sharing on social media, which draws much research attention to understanding the contents of images. Image clustering plays an important role in image understanding systems. Often, most of the existing image clustering algorithms flatten digital images that are originally represented by matrices into 1D vectors as the image representation for the subsequent learning. The drawbacks of vector-based algorithms include limited consideration of spatial relationship between pixels and computational complexity, both of which blame to the simple vectorized representation. To overcome the drawbacks, we propose a novel image clustering framework that can work directly on matrices of images instead of flattened vectors. Specifically, the proposed algorithm simultaneously learn the clustering results and preserve the original correlation information within the image matrix. To solve the challenging objective function, we propose a fast iterative solution. Extensive experiments have been conducted on various benchmark datasets. The experimental results confirm the superiority of the proposed algorithm.
Proposal Using the Cloud Architecture in System for the Early Detection of Infectious Diseases in Elderly People Fed by Biosensors Records

A joint medical and computing research group has developed a full system to build a clinical database (DB) in the Cloud to extract knowledge for the speed diagnosis of infectious diseases. It is a significant step forward as traditionally the lack of data prevents the development of medical recommenders to anticipate the diagnosis. In addition, the steadily data update enables efficient telecare for individual patients, raising the satisfaction level of the patient and relatives. The usability of the system have been tested with satisfactory results.
Mapping a quantum walk by tuning the coupling coefficient

We present a method to map the evolution of photonic quantum walks that is compatible with nonclassical input light. Our approach leverages a newly developed flexible waveguide platform to reconfigure the jumping rate between spatial modes, allowing the observation of a range of evolution times in a chip of fixed length. In a proof-of-principle demonstration, we reconstruct the evolution of photons through a uniform array of coupled waveguides by monitoring the end-face while tuning the device. This approach enables direct observation of mode occupancy at arbitrary resolution, extending the utility of photonic quantum walks for quantum simulations and related applications.
EmuStream—An End-to-End Platform for Streaming Video Performance Measurement

Cord-cutting has spread like wildfire as streaming video become commonplace in the Internet. This motivated intensive research in adaptive video streaming to improve its quality-of-experience (QoE) in the presence of network quality variations. Much of the existing research either employed dummy video contents or open source videos for QoE evaluation which may not capture the full spectrum of characteristics of real-world contents. This work fills this gap by developing a novel EmuStream platform based on real-world contents to enable realistic experiments and evaluation of any adaptive streaming algorithm in any streaming platform. First, EmuStream offers the largest (700+ titles) publicly available video bitrate trace dataset derived from real-world video contents. Second, we developed a mathematical model based on the dataset which can generate bitrate trace data for arbitrary target bitrates without actual video encoding. Third, we developed a novel Virtual Video Generator (VVG) which can generate h.264/h.265-compliant virtual videos that share the same frame/segment sizes as the source videos to enable experiments mimicking the streaming of commercial video contents. Last but not least, we developed a novel Streaming Performance Meter (SPM) that can measure detailed frame-by-frame playback performance from a video recording of the streaming playback of a virtual video. By decoding the virtual video's specially-coded frames, SPM can determine the playback timing and bitrate selected for each frame for use in computing any desired QoE metric. This paper presents the EmuStream platform and validates the trace data estimation model and VVG/SPM tools via controlled experiments.
F-Lingo: Integrating lexical feature identification into MOOC platforms for learning professional and academic English

F-Lingo is a chrome extension that works on top of the FutureLearn MOOC platform to support content-based language learning of domain-specific terminology for professional and academic purposes.
Investigation of the Effect of Concept Drift on Data-Aware Remaining Time Prediction of Business Processes

Process Mining is a rather new research area in artificial intelligence with handles event logs usually recorded by information systems. Although, remaining time prediction of ongoing business instances has been always a research question in this area, most of the existing literature does not take into account dynamicity of environment and the underlying process commonly known as concept drift. In this paper, a two-phase approach is presented to predict the remaining time of ongoing process instances; in the first phase, future path of process instances is predicted using an annotated transition system with Fuzzy Support Vector Machine probabilities based on case data and in the second phase, the remaining time is predicted by summing up the duration of future activities each estimated by Support Vector Regressor. Finally, a concept drift adaptation method is proposed. To benchmark the proposed prediction method along with the proposed concept drift adaptation method, experiments are conducted using a real-world event log and a simulation event log. The results show that the proposed approach gained 13% improvement on remaining time prediction in case of concept drift.
SCRIP: Secure Random Clock Execution on Soft Processor Systems to Mitigate Power-based Side Channel Attacks

Power-based side channel attacks are effective in revealing the secret keys of cryptographic algorithm implementations running on soft processor systems. This paper, for the first time, proposes a clock random execution methodology (referred to as SCRIP) to execute a soft processor core and most of the components (some components cannot be executed with a random clock frequency). An open source soft processor system (LowRISC which is based on the RISC-V Instruction Set Architecture) has been executed with the proposed SCRIP clock random execution methodology. Power analysis attacks (including preprocessing techniques to remove the effects of random execution) were carried out against the SCRIP LowRISC soft processor implementation to test the effects of random clock execution. SCRIP LowRISC implementation is shown to be secure for up to 300,000 encryptions, while the LowRISC implementation without SCRIP revealed the secret key within 1,000 encryptions. The result of information leakage test shows that the secret key cannot be recovered with 99.999% confidence level. Compared to other soft core processor countermeasures, SCRIP LowRISC implementation has the smallest complete soft processor system with 1.04× resource overhead (the smallest hardware masking countermeasure, which is applied to only the ALU of a RISC-V processor has 1.59× resource overhead, and the smallest balancing countermeasure soft processor, with the countermeasure applied only to the ALU and the memory, required 1.15× area overhead) where the security against power analysis attacks is applied to most components of the processor (including ALU, caches, Block RAM, Block RAM controller, bus interconnect and SD card interface). SCRIP LowRISC implementation is the first soft processor with a random execution-based countermeasure to withstand preprocessing methods (such as power trace alignment and noise filtering) which remove the effects of random execution.
Does Good Governance Affect Globalization? A case of Bangladesh

Development, in part, depends on the institutional quality of a state administration (good governance) to respond to the external affair (globalization). To corroborate it, the study aims to investigate the effect of good governance on globalization by employing both Auto Regressive Distributive Lag (ARDL) and VAR-based Variance Decomposition (VDC) estimation techniques in the context of Bangladesh over the period 1990-2016. In addition, almost similar result is found in this nexus while using both two techniques—ARDL and VDC. The study finds that there appears a co-integration between good governance and globalization in which ‘voice and accountability’ affects globalization significantly both in the long and short run. ‘Political stability and no violence’ has significantly negative impact both in the long and short run. Besides, government effectiveness, rule of law, regulatory quality and control of corruption have no substantial impact on globalization in both long and short run. Therefore, the study suggests taking appropriate policy measures to galvanize the core governance/good governance mechanisms to reach the aspired trajectory of globalization.
Learning to Auto Weight: Entirely Data-Driven and Highly Efficient Weighting Framework

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework finds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efficiency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can find a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.
A New Era with Blockchain Technology in Saudi Arabia

Recently, digital developments have led to revolutionary new technologies such as the Internet, smartphones, smart machines and digital currency, and have changed culture and concepts around the world.Culture represents the identity of the human environment and the development and openness of society in the transmission of information and knowledge to generations.In both public and private sectors, sensitive information with the integrity of data should remain safe and confidential and cannot be infringed upon, which constitutes the underlying principle of the new technology, blockchain.To that end, in this paper we survey research related to blockchain in the last four years from 2015 to 2018.We then explore the implications of this new blockchain technology in the Kingdom of Saudi Arabia.On the basis of this study, recommendations on future blockchain technology research directions in Saudi Arabia are also provided.
Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method

Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.
Cross-lingual similar document retrieval methods

In this paper, we compare different methods for cross-lingual similar document retrieval. We focus on Russian-English language pair. We compare well-known methods like Cross Lingual Explicit Semantic Analysis (CL-ESA) with methods based on cross-lingual embeddings. We use approximate nearest neighbor (ANN) search to retrieve documents based entirely on distances between learned document embeddings. Also we employ a more traditional approach with usage of inverted index, with extra step of mapping top keywords from one language to other with the help of cross-lingual word embeddings. We use Russian-English aligned Wikipedia articles to evaluate all approaches. Conducted experiments show that an approach with inverted index achieves better performance in terms of recall and MAP than other methods.
New modification on feistel DES algorithm based on multi-level keys

The data encryption standard (DES) is one of the most common symmetric encryption algorithms, but it experiences many problems. For example, it uses only one function (XOR) in the encryption process, and the combination of data is finite because it occurs only twice and operates on bits. This paper presents a new modification of the DES to overcome these problems. This could be done through adding a new level of security by increasing the key space (using three keys) during the 16 rounds of the standard encryption algorithm and by replacing the predefined XOR operation with a new # operation. Our proposed algorithm uses three keys instead of one. The first key is the input key used for encrypting and decrypting operations. The second key is used for determining the number of bits, while the third key is used for determining the table numbers, which are from 0 to 255. Having evaluated the complexity of our proposed algorithm, the results show that it is the most complex compared with the well-known DES and other modified algorithms. Consequently, in our proposed algorithm, the attacker try a number of attempts 21173 at minimum to decrypt the message. This means that the proposed DES algorithm will increase the security level of the well-known DES.
Intercriteria analysis applied to university ranking system of Turkey

In the current investigation, an application of the InterCriteria Analysis over a daaset of university rankings of Turkey is discused.InterCriteria Analysis is used to deremine possible dependencies or independencies between indicators used for universities evaluation.The comparison of the results is presented.The obtained results can be helpful for improvement of the universities estimation process using the appropriate parameters.
Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering

Semantic matching is a basic problem in natural language processing, but it is far from solved because of the differences between the pairs for matching. In question answering (QA), answer selection (AS) is a popular semantic matching task, usually reformulated as a paraphrase identification (PI) problem. However, QA is different from PI because the question and the answer are not synonymous sentences and not strictly comparable. In this work, a novel knowledge and cross-pair pattern guided semantic matching system (KCG) is proposed, which considers both knowledge and pattern conditions for QA. We apply explicit cross-pair matching based on Graph Convolutional Network (GCN) to help KCG recognize general domain-independent Q-to-A patterns better. And with the incorporation of domain-specific information from knowledge bases (KB), KCG is able to capture and explore various relations within Q-A pairs. Experiments show that KCG is robust against the diversity of Q-A pairs and outperforms the state-of-the-art systems on different answer selection tasks.
Requirements for an IoT Middleware for Utility-Scale Distributed Solar Farms

The emergence of Internet of Things (IoT) has revolutionized the way people think about computational intelligence in real-life applications. It brought to life concepts such as smart cities, which could not have been realized using classic computing technologies. While domains like health, manufacturing, and agriculture have gained greatly from the introduction of internet-connected, low-cost smart monitoring and control devices, the energy sector remains the most significant application of IoT technologies. The most recent works on smart energy systems focus on the integration of renewable energy sources such as solar and wind. While renewable energy has been a key topic of research for decades, its adoption at large scale has been hindered by reliability and intermittency issues. Internet of Things can provide solutions to overcome such issues and enable grid integration by providing means for remote and real-time monitoring and performance analysis of solar facilities. This paper presents smart solar energy systems as an IoT application and derives primary system requirements of IoT middleware required for building efficient, scalable, and reliable grid-integrated large-scale and distributed smart solar farms.
System Identification with Time-Aware Neural Sequence Models

Established recurrent neural networks are well-suited to solve a wide variety of prediction tasks involving discrete sequences. However, they do not perform as well in the task of dynamical system identification, when dealing with observations from continuous variables that are unevenly sampled in time, for example due to missing observations. We show how such neural sequence models can be adapted to deal with variable step sizes in a natural way. In particular, we introduce a ‘time-aware’ and stationary extension of existing models (including the Gated Recurrent Unit) that allows them to deal with unevenly sampled system observations by adapting to the observation times, while facilitating higher-order temporal behavior. We discuss the properties and demonstrate the validity of the proposed approach, based on samples from two industrial input/output processes.