Reviewerly/exHarmony huggingface dataset:
Fast and effective techniques for T-count reduction via spider nest identities

In fault-tolerant quantum computing systems, realising (approximately) universal quantum computation is usually described in terms of realising Clifford+T operations, which is to say a circuit of CNOT, Hadamard, and $\pi/2$-phase rotations, together with T operations ($\pi/4$-phase rotations). For many error correcting codes, fault-tolerant realisations of Clifford operations are significantly less resource-intensive than those of T gates, which motivates finding ways to realise the same transformation involving T-count (the number of T gates involved) which is as low as possible. Investigations into this problem [arXiv:1206.0758, 1303.2042, 1308.4134, 1601.07363, 1606.01904, 1701.00140] has led to observations that this problem is closely related to NP-hard tensor decomposition problems [arXiv:1712.01557] and is tantamount to the difficult problem of decoding exponentially long Reed-Muller codes [arXiv:1601.07363]. This problem then presents itself as one for which must be content in practise with approximate optimisation, in which one develops an array of tactics to be deployed through some pragmatic strategy. In this vein, we describe techniques to reduce the T-count, based on the effective application of "spider nest identities": easily recognised products of parity-phase operations which are equivalent to the identity operation. We demonstrate the effectiveness of such techniques by obtaining improvements in the T-counts of a number of circuits, in run-times which are typically less than the time required to make a fresh cup of coffee.
A systematic review of research on e-book-based language learning

This study presents a systematic review of previous research on e-book-based language learning. E-book-based language learning has been increasingly investigated in the field of language education, whereas previous review studies of relevant literature were relatively low in volume and incomprehensive from aspects of research participants and research perspectives. In the present study, we identified 52 relevant journal articles on e-book-based language learning published in the recent decade, coding them from three dimensions: methodology, e-book devices and features, and research issues and findings. The results indicated that: (a) most studies on e-book-based language learning were of a large sample size and one-session investigation; (b) pre-school children and university students were the most frequently investigated sample groups; (c) computers and tablets were the most frequently used devices; (d) multimedia, feedback giving, gamification and personalisation were the four features most frequently applied in enhancing the efficiency of e-book-based language learning; (e) researchers collected and processed data mainly through pre- and post-tests and group comparisons; (f) learning outcomes, learning behaviours and learners’ psychological states were investigated in most studies, whereby the findings indicated overall positive effects of e-book on language learning.
Cyber-security effect on organizational internal process: mediating role of technological infrastructure

Adopting the technologies among organizations comes with the continuous worries of protection and hacking. The idea of cyber-security has become over the years the main interest of many organizations, which depend on technologies in its operations, which requires them to pay extra attention to their technological infrastructure. The current study aims at examining the influence of cyber-security forces on organizational internal operations and the role of technological infrastructure in defining and controlling the level of protection that cyber-security has on organizational internal processes. Quantitative approach was adopted, and a questionnaire was utilized to collect the data from a convenient sample of 360 software engineers, network engineers, software testers, web developers, and technical support using a structured survey questionnaire, and analyzed using SPSS version 21. The results confirmed that cyber-security motivators (data growth, technology expansion, access to required resources, operational control, and technical control) indirectly affect solid internal processes that are attributed to the consistency of technological infrastructure in an organization. The variable of ‘data growth’ appeared to be the most influential motivator on cyber-security strategies, as it scored a mean of 4.2661, which is the highest among all adopted variables and followed by the variable of ‘technical control’, which scored a mean of 4.1296. Accordingly, the study recommends that organizations should consider IT infrastructure as a main item within their risk management strategies to avoid unpredicted risks and attacks.
Plan S y ecosistema de revistas españolas de ciencias sociales hacia el acceso abierto: amenazas y oportunidades

Plan S promotes that all articles from research funded by public financing agencies that subscribe it, have to be publicly available immediately, either in open access journals or in repositories that meet certain conditions, as of January 2021. Plan S has been promoted by Science Europe, a group of public research funding agencies through the cOAlition S, and has published two versions that have generated intense academic debate worldwide. Even though no public or private research agency of Spain has been committed at the moment to support Plan S, it is interesting to assess how this Plan can affect the ecosystem of Spanish scientific journals. We present the results of the analysis of the Spanish journals of SCImago Journal Rank in Social Sciences and the compliance that they have with the requirements that cOAlition S has described for the journals to be considered of Open Access of quality. These requirements are related to the opening of the content, the payment model, technical aspects and copyright. It is concluded that although most journals meet the requirements related to open content, they are left with challenges related to the technical aspects of maximum interoperability and reuse. Journals are recommended to take the Plan S as an opportunity and take advantage to carry out a full digital transformation.
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion

Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space. Against this trend, we propose an approach called AnyBURL that is rooted in the symbolic space. Its core algorithm is based on sampling paths, which are generalized into Horn rules. Previously published results show that the prediction quality of AnyBURL is on the same level as current state of the art with the additional benefit of offering an explanation for the predicted fact. In this paper, we are concerned with two extensions of AnyBURL. Firstly, we change AnyBURLs interpretation of rules from $Θ$-subsumption into $Θ$-subsumption under Object Identity. Secondly, we introduce reinforcement learning to better guide the sampling process. We found out that reinforcement learning helps finding more valuable rules earlier in the search process. We measure the impact of both extensions and compare the resulting approach with current state of the art approaches. Our results show that AnyBURL outperforms most sub-symbolic methods.
Flaw Label: Exploiting IPv6 Flow Label

The IPv6 protocol was designed with security in mind. One of the changes that IPv6 has introduced over IPv4 is a new 20-bit flow label field in its protocol header.We show that remote servers can use the flow label field in order to assign a unique ID to each device when communicating with machines running Windows 10 (versions 1703 and higher), and Linux and Android (kernel versions 4.3 and higher). The servers are then able to associate the respective device IDs with subsequent transmissions sent from those machines. This identification is done by exploiting the flow label field generation logic and works across all browsers regardless of network changes. Furthermore, a variant of this attack also works passively, namely without actively triggering traffic from those machines.To design the attack we reverse-engineered and cryptanalyzed the Windows flow label generation code and inspected the Linux kernel flow label generation code. We provide a practical technique to partially extract the key used by each of these algorithms, and observe that this key can identify individual devices across networks, VPNs, browsers and privacy settings. We deployed a demo (for both Windows and Linux/Android) showing that key extraction and machine fingerprinting works in the wild, and tested it from networks around the world.
Graph Databases for Information Retrieval

Graph models have been deployed in the context of information retrieval for many years. Computations involving the graph structure are often separated from computations related to the base ranking. In recent years, graph data management has been a topic of interest in database research. We propose to deploy graph database management systems to implement existing and novel graph-based models for information retrieval. For this a unifying mapping from a graph query language to graph based retrieval models needs to be developed; extending standard graph database operations with functionality for keyword search. We also investigate how data structures and algorithms for ranking should change in presence of continuous database updates. We want to investigate how temporal decay can affect ranking when data is continuously updated. Finally, can databases be deployed for efficient two-stage retrieval approaches?
LETHE: Forgetting and Uniform Interpolation for Expressive Description Logics

Abstract Uniform interpolation and forgetting describe the task of projecting a given ontology into a user-specified vocabulary, that is, of computing a new ontology that only uses names from a specified set of names, while preserving all logical entailments that can be expressed with those names. This is useful for ontology analysis, ontology reuse and privacy. Lethe is a tool for performing uniform interpolation on ontologies in expressive description logics, and it can be used from the command line, using a graphical interface, and as a Java library. It furthermore implements methods for computing logical difference and performing abduction using uniform interpolation. We present the tool together with an evaluation on a varied corpus of realistic ontologies.
Pentagram Sustainability Model

This paper systematically reviews the current approaches of assessing sustainability of Neighbourhood-level Urban Communities (NLUCs) to identify existing dimensions of sustainability including missing and neglected ones. The authors have adopted a systematic literature review (SLR) approach to provide an overview of state-of-the-art literature in urban sustainability assessment and measurement. The novelty of the content analysis is that it has been accomplished using an automated process of keyword extraction from the systematically selected literature using a Python-implementation of the popular Rapid Automatic Keyword Extraction (RAKE) algorithm. Finally, the paper proposes a pentagram that successfully incorporates five dimensions of sustainability: environmental, economic, social, institutional and cultural, with an overarching component of innovation likely to have an impact on each due to technological advancements and future trends. The outcome of this model will be a basic framework within which indicators can be assigned for assessing the sustainability of the NLUCs.
ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking

One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets, typically done via a scoring function. Despite the great advances in MOT, designing a reliable scoring function remains a challenge. In this paper, we introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion. One key property of our model is its ability to generate multiple likely futures of a tracklet given partial observations. This allows us to not only score tracklets but also effectively maintain existing tracklets when the detector fails to detect some objects even for a long time, e.g., due to occlusion, by sampling trajectories so as to inpaint the gaps caused by misdetection. Our experiments demonstrate the effectiveness of our approach to scoring and inpainting tracklets on several MOT benchmark datasets. We additionally show the generality of our generative model by using it to produce future representations in the challenging task of human motion prediction.
Aspect-Based Academic Search Using Domain-Specific KB

Academic search engines allow scientists to explore related work relevant to a given query. Often, the user is also aware of the aspect to retrieve a relevant document. In such cases, existing search engines can be used by expanding the query with terms describing that aspect. However, this approach does not guarantee good results since plain keyword matches do not always imply relevance. To address this issue, we define and solve a novel academic search task, called aspect-based retrieval, which allows the user to specify the aspect along with the query to retrieve a ranked list of relevant documents. The primary idea is to estimate a language model for the aspect as well as the query using a domain-specific knowledge base and use a mixture of the two to determine the relevance of the article. Our evaluation of the results over the Open Research Corpus dataset shows that our method outperforms keyword-based expansion of query with aspect with and without relevance feedback.
Trustworthiness and a Zero Leakage OTMP-P2L Scheme Based on NP Problems for Edge Security Access

Resource constraints have prevented comprehensive cryptography and multifactor authentication in numerous Internet of Things (IoT) connectivity scenarios. Existing IoT systems generally adopt lightweight security protocols that lead to compromise and privacy leakage. Edge computing enables better access control and privacy protection, furthermore, blockchain architecture has achieved a trusted store of value by open-source and distributed consensus mechanisms. To embrace these new paradigms, we propose a scheme that employs one-time association multitasking proofs for peer to local authentication (OTMP-P2L). The scheme chooses relevant nondeterministic polynomial (NP) problem tasks, and manages localized trust and anonymity by using smart devices such as phones and pads, thereby enabling IoT devices to autonomously perform consensus validation with an enhanced message authentication code. This nested code is a one-time zero-knowledge proof that comprises multiple logic verification arguments. To increase diversity and reduce the workload of each one, these arguments are chained by a method that establishes some of the inputs of the following task from the output of previous tasks. We implemented a smart lock system and confirmed that the scheme outperforms IoT authentication methods. The result demonstrates superior flexibility through dynamic difficulty strategies and succinct non-interactive peer-to-peer (P2P) verification.
Evaluation of Teaching Assistant Robot for Programming Classes

This paper describes a robot for supporting teachers who are teaching computer programming classes.In its latest series of "Courses of Study", the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan strongly recommends the use of active learning systems and the introduction of computer programming education courses in elementary schools.Programming instruction typically includes explanations of the basic syntax used in programming languages, the application of that syntax, and related exercises.However, the teachers in programming classes commonly spend much more time correcting errors, that is, debugging their students' programs, than they spend teaching.These delays can affect the progress of programming lessons and the motivation of the participating students.With these points in mind, we have developed a teaching assistant robot that is designed to support efficient classroom management of programming classes by advising and assisting students who are encountering problems.Herein, we describe the tasks performed by the teaching assistant robot in a classroom environment in which actual programming lessons are assumed to be taught.In particular, we explain the problems encountered by students in the process of learning basic programming techniques, the causes of the problems, the method by which the teaching assistant robot identifies those problems, and the contents of advice provided by the teaching assistant robot corresponding to those problems.We also show the effectiveness of the teaching assistant robot by conducting evaluation experiments.
MOIN: A Nested Sequent Theorem Prover for Intuitionistic Modal Logics (System Description)

We present a simple Prolog prover for intuitionistic modal logics based on nested sequent proof systems. We have implemented single-conclusion systems (Gentzen-style) and multi-conclusion systems (Maehara-style) for all logics in the intuitionistic modal IS5-cube. While the single-conclusion system are better investigated and have an internal cut-elimination, the multi-conclusion systems can provide a countermodel in case the proof search fails. To our knowledge this is the first automated theorem prover for intuitionistic modal logics. For wider usability, we also implemented classical normal modal logics in the S5-cube.
IntelliBVR - Intelligent Large-Scale Video Retrieval for Objects and Events Utilizing Distributed Deep-Learning and Semantic Approaches

There is increasing reliance on the intelligent CCTV systems for effective analysis and interpretation of the streaming data to recognize activities and to ensure public safety. Monitoring videos captured by surveillance cameras is always a challenging and time-consuming task. There is a need for automated analysis using computer vision methods in order to extract spatial and temporal features to assist the authorities. Once videos are processed using computer vision technologies, another issue is how to index the extracted low-level features to search, analyze, and browse? How to bridge the semantic gap between the low-level features in Euclidean space and temporal relation across videos in a multi-stream environment? Similarly, how to deal with petascale video in the cloud while extracting the low-level and high-level features? In order to address such issues, in this paper, we propose a layered architecture for large-scale distributed intelligent video retrieval while exploiting deep-learning and semantic approaches called IntelliBVR. The base layer is responsible for large-scale video data curation. The second and the third layer is supposed to process and annotate videos, respectively while using deep learning on the top of distributed in-memory computing engine. Finally, the knowledge curation layer, where the extracted low-level and high-level features are mapped to the proposed ontology so that it can be searched and retrieved using semantic rich queries. Finally, we implement and show results, which project the effectiveness of IntelliBVR.
Enhanced Model for Cloud Data Security based on Searchable Encryption and Hybrid Fragmentation

Cloud computing is a new technology that transfer the computing process from personal computers into cloud servers over the internet. Nevertheless, as the client information data is stored in the cloud provider servers, the confidentiality of the information become a new concern. Different algorithms based on Encryption is presented previously to provide cloud clients with confidentiality. The main idea of encryption algorithms for cloud data is to permit cloud clients queries to be handled using encrypted data without decryption. This paper presents a new security mechanism using hybrid method of encryption algorithms and a distribution system to enhance cloud database confidentiality. A vertical fragmentation technique is adopted from alsirhani's model for distributing data over clouds. However, to overcome a weakness in alsirhani's model where compromises to a fragment can still make data meaningful. Instead, the proposed model uses a hybrid fragmentation technique to make data on fragments meaningless if compromised. The proposed model distributes the cloud database among the clouds using the provider views and level of confidentiality that is delivered by the employed encryption algorithms. To evaluate the proposed searchable encryption and hybrid fragmentation model, the study developed a Java application for simulating the hybrid cloud. The simulation combines public and private clouds; as essential processes is conducted inside the private cloud. The evaluation of the work was conducted by comparing the proposed model with existing solutions in query response and security characteristics. Preliminary results showed that the proposed searchable encryption and hybrid fragmentation model provides a secure mechanism that enhances data confidentiality in terms of faster response and additional security.
Improved Internal Model Control Technique for Position Control of AC Servo Motors

This paper focuses on the simulation analysis of the conventional Internal Model Control (IMC) technique and the development of two proposed control techniques for the position control of AC Servo Motor. Internal Model Control (IMC) technique [1] was only able to control the AC Servo Motor under static load condition. Also, it had step response problems, and it was not robust against external disturbances. For these reasons, the IMC technique was further improved to control the AC Servo Motor under dynamic load conditions by proposing Amended Internal Model Controller (AIMC). The step response and the robustness of AIMC against external disturbances were further improved by proposing AIMC+FLC. Where a Fuzzy Logic Controller (FLC) is designed and connected with the AIMC.
Unlocking the Power of Artificial Intelligence for Your Business

Every day, we deal dozens of times with artificial intelligence applications such as autonomous cars, spam filters, or voice recognition systems. Often we do so without explicitly knowing that those applications are based on AI because AI technology has become mainstream in the last 10 years. From a business perspective, AI enables us to automate human decision making. We can thus cut costs and waiting times as well as increase revenue and profit margin. However, we have just started to scratch the surface as there are many more AI opportunities for companies to be exploited. This chapter first provides a gentle, intuitive introduction to AI for the interested business decision maker. In the second part, this chapter provides advice and best practices on how to rethink your business in order to become an AI-driven business that prospers in an ever more competitive environment.
Examining the Irregularities of Articles and Introducing Minimized NP Systems in Unish

The current research carefully reviews article systems in existing natural and artificial languages.It also shows various denotations of article systems in a series of languages, arguing that irregularities of article use across the languages pose ongoing difficulties to language learners.The current study suggests that the Unish language is advantageous to learn in terms of the article system because it only marks specificity, which can be argued to be minimally represented