Reviewerly/exHarmony huggingface dataset:
The Need for Structure in Quantum LDPC Codes

Existence of quantum low-density parity-check (LDPC) codes whose minimal distance scales linearly with the number of qubits is a major open problem in quantum information. Its practical interest stems from the need to protect information in a future quantum computer, and its theoretical appeal relates to a deep "global-to-local" notion in quantum mechanics: whether we can constrain long-range entanglement using local checks. Given the inability of lattice-based quantum LDPC codes to achieve linear distance, research has recently shifted to the other extreme end of topologies, so called high-dimensional expanders. In this work we show that trying to leverage the mere "random-like" property of these expanders to find good quantum codes may be futile: quantum CSS codes of $n$ quits built from $d$-complexes that are $\varepsilon$-far from perfectly random, in a well-known sense called discrepancy, have a small minimal distance. Quantum codes aside, our work places a first upper-bound on the systole of high-dimensional expanders with small discrepancy, and a lower-bound on the discrepancy of skeletons of Ramanujan complexes due to Lubotzky.
Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

It is critical for quality requirements, such as trust, privacy, and confidentiality, to be fulfilled during the execution of smart city applications. In this study, smart city applications were modeled as agent systems composed of many agents, each with its own privacy and confidentiality properties. Violations of those properties may occur during execution due to the dynamic of agent behavior, decision-making capabilities, and social activities. In this research, a framework called Agent Quality Management was proposed and implemented to manage agent quality in agent systems. This paper demonstrates the effectiveness of the approach by applying it toward a smart city application called a crowdsourced navigation system to verify and assess agent data confidentiality. The AnyLogic Agent-Based Modeling tool was used to model and conduct the experiments. The experiments showed that the framework helped to improve the detection of agent quality violations in a dynamic smart city application. The results can be further analyzed using advanced data analytic approach to reduce future violations and improve data confidentiality in a smart city environment.
Usability of Development Tools: A CASE-Study

To facilitate model-driven development in practice, we need tools that support and empower developers. Many of the tools for it, however, are somewhat lacking with respect to usability, which can act as a major obstacle in adopting a model-driven approach and impede productivity. Efforts to find concrete usability flaws in model-driven tools are still relatively rare, and in the human computer interaction community, usability for highly specialized expert software is also not a prominent topic. In this work we conduct an evaluation using a number of usability evaluations methods on the 4diac IDE as an example of a model-driven engineering tool to see what works and where the problems are. With those different methods we found a considerable number of usability issues of varying severity as well as some indication which methods work in what context. These findings of course help improving the 4diac IDE specifically but also offer more general insights that may benefit other tools in improving their user experience. Overall our evaluation shows that there is a definite need for better usability and more evaluation of it.
Performance Analysis of 128-bit Modular Inverse Based Extended Euclidean Using Altera FPGA Kit

Abstract Modular inverse is a division operation performed over a modulus and is considered as a fundamental operation for many public-key cryptosystems. Extended Euclidean Algorithm (EEA) is considered one of the most efficient algorithms to compute the modular multiplicative inverse of two coprime numbers. In this paper, we are reporting on the FPGA implementation of 128-bit modular inverse unit using Extended Euclidean Algorithm (EEA) algorithm with maximum possible parallelism of the internal operations. To verify the deposed implementation, we have synthesized our VHDL coding using ALTERA Cyclone IV FPGA with target device EP4CGX22CF19C7 using Quartus II simulation package. The experimental results showed that the proposed implementation recorded a critical path delay of 16.84 ns with maximum operational frequency of 72.7 MHz. Also, the area of the proposed design was estimated as the number of logic elements (LEs) utilized by the proposed unit which is reported as 7157 LEs. Finally, the power dissipation of the proposed using was estimated as the total FPGA thermal power dissipation and reported as 231.41 mW. Hence, the proposed modular inverse module can be efficiently embedded with many FPGA based cryptographic applications.
Note-taking as communication design: promoting democratic processes

Whereas notes are ubiquitous to democratic meeting designs, note-taking practices within public participation processes remain taken-for-granted. We argue that note-taking is a communication practice that calls for cultivating expertise and critical reflexivity. Employing Communication as Design (CAD) we analyze communication design logics for note-taking active in public processes and the problems encountered enacting these design logics. CAD analysis illuminates four design logics underpinned by democratic values: notes as summary, data points, quotables, and critical voice. Building from this empirical analysis, democratic note-takers must be sensitive to the need for coordinating design logics to process goals; yet multiple design logics are often necessary to uphold multiple democratic values.
Prototype Container-Based Platform for Extreme Quantum Computing Algorithm Development

Recent advances in the development of the first generation of quantum computing devices have provided researchers with computational platforms to explore new ideas and reformulate conventional computational codes suitable for a quantum computer. Developers can now implement these reformulations on both quantum simulators and hardware platforms through a cloud computing software environment. For example, the IBM Q Experience provides the direct access to their quantum simulators and quantum computing hardware platforms. However these current access options may not be an optimal environment for developers needing to download and modify the source codes and libraries. This paper focuses on the construction of a Docker container environment with Qiskit source codes and libraries running on a local cloud computing system that can directly access the IBM Q Experience. This prototype container based system allows single user and small project groups to do rapid prototype development, testing and implementation of extreme capability algorithms with more agility and flexibility than can be provided through the IBM Q Experience website. This prototype environment also provides an excellent teaching environment for labs and project assignments within graduate courses in cloud computing and quantum computing. The paper also discusses computer security challenges for expanding this prototype container system to larger groups of quantum computing researchers.
Geological Survey of Canada 8.0: mapping the journey towards predictive geoscience

Abstract The Geological Survey of Canada (GSC) has been furthering the geoscientific understanding of Canada since its inception in 1842, the equivalent of seven generations ago. The evolution of the activities of the GSC over this period has been driven by evolving geographic, economic and political contexts and needs. Likewise, new technologies and evolving scientific methods and models shaped broadly the successive generations of GSC geoscience activities. The most recent GSC generation presented a mixed portfolio of large framework mapping geoscience programmes, and more targeted, hypothesis-driven geoscience research, and the development of decision support products for a range of government, industry and other stakeholders needs. Entering its eighth generation, the GSC and related organizations are embracing digital technologies for applications such as the evaluation of mineral resource potential, the evaluation of risks and the early warning of earthquakes. In order to do so, the GSC will need to develop new methods and systems in co-operation with other geological survey organizations, and target its data acquisition and research to further advance its ability to respond to the evolving needs of society to navigate geology through space and time, from the past to the present, and from the present to the future.
Reconstructing cached video stream content:- Part 2

In 2018, Horsman (2018a; 2018b) provided guidance for the reconstruction of cached stream remnants following use of the Periscope, Facebook Live and YouTube platforms. These works confirmed that video stream content can be cached to a local device when viewed via an Internet browser, and that following the provided methodology, video content can be rebuilt for subsequent viewing. This work provides an analysis of a further six platforms; Twitch, Youtube Live, Mixer, Ustream.tv, Smashcast.tv, and Younow. An analysis of localised stream caching is provided along with methodologies to view this content, ensuring support to those investigating offences where streaming may provide pertinent evidence of a criminal act or in fact be illegal per se.
Deep Imputation of Temporal Data

Predictive modeling in healthcare has shown promise in various settings, such as early diagnosis, discovery of genotypephenotype associations, and the optimization of medical resource allocations [1]. Due to their data-driven nature, the effectiveness of these studies heavily relies on the quality of the collected data. However, clinical domain is often driven by business processing needs, such that ancillary information, which would be useful for building predictive models, is often collected in a haphazard fashion. As a consequence, the data are often incomplete. This is a challenge for learning effective predictive models, which only compounds in complexity in multivariate time series. This is because 1) filtering out instances that contain missing values (e.g., the feature vector associated with a particular time step) can induce bias in the model learning process [2], 2) in practice, it is difficult for learned models to make ad hoc predictions on test cases with multiple missing features, and 3) applying ineffective imputation strategies can result in the loss of noteworthy patterns [3] and learning models that are simply incorrect.
A Deep Learning Model with Data Enrichment for Intent Detection and Slot Filling

A model for accurately solving the tasks of intent detection and slot filling requires a substantial amount of user queries annotated manually, which is time-consuming and costly. In this study, in order to circumvent this problem, we propose a method of expanding a training set for handling these two tasks. The data are augmented by applying a random mutation to the training samples, following a set of heuristic rules. For validation of the method, we construct a compound neural architecture composed of a long short-term memory layer, an attention mechanism, and conditional random fields. The experiments conducted on the Automatic Terminal Information Service (ATIS) dataset demonstrate that the models trained on the expanded datasets improve their F-score for the slot-filling task. This improvement is particularly significant for small datasets. We believe that this method allows for the expansion of a small set of previously annotated user queries into a large training set that is sufficient for correctly constructing a model of intent detection and slot filling. Therefore, our approach can be used to save time and money by reducing the amount of data required to train a natural language understanding model. Its novelty consists in expanding a training set for a deep learning model based on a random mutation of training samples, following a set of heuristic rules that utilise external lexicons and the training data itself.
Incremental k-Means Method

In the last few decades, k-means has evolved as one of the most prominent data analysis method used by the researchers. However, proper selection of k number of centroids is essential for acquiring a good quality of clusters which is difficult to ascertain when the value of k is high. To overcome the initialization problem of k-means method, we propose an incremental k-means clustering method that improves the quality of the clusters in terms of reducing the Sum of Squared Error ($$SSE_{total}$$). Comprehensive experimentation in comparison to traditional k-means and its newer versions is performed to evaluate the performance of the proposed method on synthetically generated datasets and some real-world datasets. Our experiments shows that the proposed method gives a much better result when compared to its counterparts.
Plaintext-Checkable Encryption with Unlink-CCA Security in the Standard Model

Plaintext-Checkable Encryption (PCE) was first proposed by Canard et al. to check whether a ciphertext encrypts a given plaintext under the public key. This primitive is very useful in many applications, e.g., search on encrypted database and group signature with verifier-local revocation (GS-VLR). In the literature, existing PCE schemes only satisfies unlink notion that defines the adversary to get information about whether two challenge ciphertexts share the same plaintext or not, without given the challenge plaintexts. Using the tool of pairing-friendly smooth projective hash function (PF-SPHF), we propose the first PCE construction with the most desirable unlink-cca notion, which is stronger than unlink by additionally providing a decryption oracle. We prove it in the standard model based on the hard subset membership problem. Finally, by instantiating SPHF from DDH assumption, we obtain a PCE instantiation from SXDH assumption and show that it achieves not only the desired security but also efficient test computation complexity. Hence it will be very useful in practical applications.
Classification using Discriminative Restricted Boltzmann Machines on Spark

Apache Spark is a popular open-source framework for distributed execution of data analytics and machine learning tasks, but with limited support for neural networks. In this work, we investigate the reasons for that and based on our findings we extend the native MLLib library with Restricted Boltzmann Machines, a common building block of deep learning architectures, along with a gradient descent optimization procedure suitable for neural networks. As a case study, we developed a handwritten digit recognition application using discriminative RBMs to demonstrate the performance of our implementation.
MB-PBA: Leveraging Merkle Tree and Blockchain to Enhance User Profile-based Authentication in E-Learning Systems

User authentication in online systems is always a big challenge in determining whether the logged-in user is the legitimate account owner. This challenge becomes critical in E-learning systems since it affects the assessment result of learners accurately. User profile-based authentication (PBA) has shown its advantages compared with other existing approaches. The PBA system will generate random questions and then evaluate the users' answers based on the collected information when the user registered into the system. However, this approach may reveal the users' data when the system is compromised and exploited. In this paper, we utilize the dominant features of the Merkle tree data structure and smart contracts on Blockchain to improve the PBA approach. The proposed method simplifies identity authentication, protects user confidential information, and enhances the security factor in terms of data integrity, user information transparency, and system availability. Our preliminary experiments have shown the potential and feasibility of the proposed authentication method.
Integral-Sliding-Mode-Observer-Based Structure and Motion Estimation of a Single Object in General Motion Using a Monocular Dynamic Camera

An integral sliding mode observer (ISMO)-based method for estimating the structure and motion (SaM) of an object in general motion is proposed using a monocular dynamic (moving) camera. As the unknown range and object velocity can be considered as disturbances and should be quickly estimated, it is necessary to maintain the robustness against these disturbances from the start by eliminating the reaching mode. Therefore, the ISMO-based method is proposed on the basis of a relative camera-object motion model. By formulating the relative motion model with three-dimensional measurable state variables, three unknown components among the range, unknown object velocity components, and unknown camera velocity components can be estimated by the proposed method in the following cases: i) the camera is in dynamic motion and the object is in semigeneral motion [i.e., initially static (stationary) and then in general (static or dynamic) motion], ii) the camera is in dynamic motion and the object is in general motion within a constrained space, and iii) both the object and camera are in general motion within a less constrained space when the range information is available. Simulation and experimental results demonstrate that the range and object velocity can be estimated with a satisfactory transient response using a monocular dynamic camera.
A Co-Occurrence Recommendation Model of Software Security Requirement

To guarantee the quality of software, specifying security requirements (SRs) is essential for developing systems, especially for security-critical software systems. However, using security threat to determine detailed SR is quite difficult according to Common Criteria (CC), which is too confusing and technical for non-security specialists. In this paper, we propose a Co-occurrence Recommend Model (CoRM) to automatically recommend software SRs. In this model, the security threats of product are extracted from security target documents of software, in which the related security requirements are tagged. In order to establish relationships between software security threat and security requirement, semantic similarities between different security threat is calculated by Skip-thoughts Model. To evaluate our CoRM model, over 1000 security target documents of 9 types software products are exploited. The results suggest that building a CoRM model via semantic similarity is feasible and reliable.
Energy Efficiency of Microring Resonator (MRR)-Based Binary Decision Diagram (BDD) Circuits

The saturation of rapid progress in transistor technology has brought us to a point where the computing systems face fundamental physical limitations. Emerging technologies propose various alternatives and photonic circuits are among promising candidates due to their high operation speed, energy efficient passive components, low crosstalk and appropriateness for parallel computation. In this work, we design a microring resonator (MRR) based Binary Decision Diagram (BDD) NAND logic gate and study its characteristics inline with a MRR-based BDD half adder circuit proposed by Wada et. al. [1]. We analyze energy efficiency limitations of BDD architectures using reversible and irreversible circuit structures. The circuits we focus on in this work are composed of silicon MRR-based switching nodes where the coupling gap and ring size play a key role in the performance of the circuits. We study the physical structure of the circuits as well as dynamics of the information processing, and calculate the fundamental lower bounds on the energy dissipation as a result of computation. We also perform extensive analyses on Lumerical MODE simulations to optimize the energy efficiency based on various factors including waveguide properties, ring radius and gap size. The results we obtain allow us to assess limitations imposed by the physical nature of MRR-based photonic circuits in computation, and compare theory against simulation and hence significantly contribute to the strategic development of this technology as a part of future computers.
Quantifying Degradations of Convolutional Neural Networks in Space Environments

Advances in machine learning applications for image processing, natural language processing, and direct ingestion of radio frequency signals continue to accelerate. Less attention, however, has been paid to the resilience of these machine learning algorithms when implemented on real hardware and subjected to unintentional and/or malicious errors during execution, such as those occurring from space-based single event upsets (SEU). This paper presents a series of results quantifying the rate and level of performance degradation that occurs when convolutional neural nets (CNNs) are subjected to selected bit errors in single-precision number representations. This paper provides results that are conditioned upon ten different error case events to isolate the impacts showing that CNN performance can be gradually degraded or reduced to random guessing based on where errors arise. The degradations are then translated into expected operational lifetimes for each of four CNNs when deployed to space radiation environments. The discussion also provides a foundation for ongoing research that enhances the overall resilience of neural net architectures and implementations in space under both random and malicious error events, offering significant improvements over current implementations. Future work to extend these CNN resilience evaluations, conditioned upon architectural design elements and well-known error correction methods, is also introduced.
Different scenarios for the teaching of mathematics with the support of virtual platforms: Flipped classroom

Abstract This text shows the initial findings of an investigation that sought to implement an alternative model for the teaching of mathematics at a public university in Norte de Santander, Colombia, using virtual platforms to support teaching. The research paradigm focuses on the mixed method to identify the positive elements and aspects to improve of this didactic proposal. Among the findings, it is highlighted that one of the difficulties in implementing this tool refers to the limitations of space in the material attached to the platform, which meant proposing only the links of the material created. Implications for teacher training and suggestions for future implementations are discussed.
Categorical models of the differential λ-calculus

Abstract The paper shows how the Scott–Koymans theorem for the untyped λ -calculus can be extended to the differential λ -calculus. The main result is that every model of the untyped differential λ -calculus may be viewed as a differential reflexive object in a Cartesian-closed differential category. This extension of the Scott–Koymans theorem depends critically on unraveling the somewhat subtle issue of which idempotents can be split so that differential structure lifts to the idempotent splitting. The paper uses (total) Turing categories with “canonical codes” as the basic categorical semantics for the λ -calculus. It develops the main result in a modular fashion by showing how to add left-additive structure to a Turing category, and then – on top of that – differential structure. For both levels of structure, it is necessary to identify how “canonical codes” must behave with respect to the added structure and, furthermore, how “universal objects” must behave. The latter is closely tied to the question – which is the crux of the paper – of which idempotents can be split while preserving the differential structure of the setting. This paper is the full version of a conference paper and includes the proofs which were omitted from that version due to page-length restrictions.