Reviewerly/exHarmony huggingface dataset:
Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering

&lt;p&gt;In response to the abnormal data mining in dam safety monitoring, and based on the traditional spectral clustering, this paper presents an anomaly detection method based on improved spectral clustering. This method applies a distance and density adaptive similarity measure. The natural eigenvalue is introduced to adaptively select the neighbors of data points, and the similarity is redefined to be combined with the natural k-nearest neighbor. Furthermore, the shared neighbor is introduced to adjust the similarity between the monitoring data samples according to the regional density. Moreover, considering the distribution of dam monitoring data, the initialization of clustering centers is optimized according to both the density and distance feature. This method can prevent the algorithm from local optimum, better adapt to the density of non-convex dataset, reduce the number of iterations, and enhance the efficiencies of clustering and anomaly detection. Taking the dam slab monitoring data as the research object, experimental datasets are formed. Experiments on these datasets further verify that the method of this paper can effectively adapt to discrete distribution datasets and is superior to the classical spectral clustering method in both clustering and anomaly detection.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Critical success factors in Software Development Projects

Irrespective of the software development methodology, projects still fail at any development stage. Some of the factors that may affect project success or failure include process-related factors, such as an unrealistic budget and schedule, or technical-related factors like the methodology. The study aims to determine the influence of the most critical success factors of each category (people, process, and technical) on the overall outcome of software projects in the South African context. This study surveyed members of software development teams from across South African organisations and gathered information about software development project success and factors that contribute to project outcomes. A structured questionnaire was used to collect the study data. According to the results, software development projects need: (1) a committed and motivated team; (2) the client must be involved all the time; (3) specifications/requirements must be clear; (4) leadership must be good; (5) the project’s goals and objectives must be well-defined. The critical success factors are ranked by their strength in relation to success criteria. The study further found that most of the perceived factors are related to non-technical factors. This research contributes to the current software industry debate about critical success factors categories in the South African context.
Mindless Memorization Booster: A Method to Influence Memorization Power Using Attention Induction Phenomena Caused by Visual Interface Modulation and Its Application to Memorization Support for English Vocabulary Learning

Memorization is necessary for various fields, such as language learning in the field of education. While memorization learning is often tedious and demotivating due to requiring conscious effort, few support approaches improve memorization unconsciously with low conscious effort. In this study, we propose a method, Mindless Memorization Booster, which improves users’ memorization unconsciously by visual stimuli of modulating the visual interface. This method is based on previous findings that the modulation of perceptual stimuli arouses attention/concentration. When the user looks at the memorization target, the proposed method presents a change in visual interface (e.g., changes in memorization target size, background color, and visual icon movement) to cause a psychological phenomenon of affecting the user’s attention and concentration, aiming at enhancing the memorization unconsciously. A prototype system of the proposed method was implemented for an English vocabulary memorization learning application. The evaluation results showed that the user’s memorization result was affected by the proposed method, and the speed of recall (i.e., outputs of the memorization word from the brain) increased by about 1 s per one memorization word without causing a negative affection on the number of correct answers for memorization. This result indicated the feasibility of the proposed method for memorization learning support. Our findings are helpful for designing visual information interfaces that consider the phenomena affecting the user’s memorization and promote memorization learning unconsciously.
A Contemporary Review of Hate Crime Legislation in the United States

Hate-motivated crime remains problematic in the United States. California passed the first hate crime law in 1978; Congress followed in 1990. States continue to amend their hate crime legislation, producing an amalgam of statutory provisions. This article creates a conceptual framework from which to classify hate crime legislation across the 50 states and Washington, DC. Laws were identified through Westlaw. Analyses compared the types of crimes covered, discrete and insular minorities protected, prosecutorial alternatives, mandates for law enforcement agencies, and additional rights provided to victims among states’ legislation. Considerable variation in scope and content of hate crime legislation exists among states, leaving several vulnerable groups unprotected, law enforcement underprepared, and victim rights and resources sparse. Future directions for hate crime policy and legislation are discussed.
Ciphertext-Policy Attribute-based Encryption for Securing IoT Devices in Fog Computing

Securing Internet of Things (IoT) devices in fog computing systems can be challenging due to the inherent limitations of IoT devices. For instance, cryptographic primitives, such as attribute-based encryption (ABE) schemes, are computationally expensive for deployment on IoT devices. Thus, ABE is not realistic in facilitating real-time updates in various applications of IoT. Therefore, attribute-based and multi-authority encryption schemes are designed that help attribute revocation and computation outsourcing from IoT devices to fog computing servers. The attribute revocation scheme is based on the ciphertext-policy attribute-based encryption (CP-ABE) technique. The CP-ABE scheme allows secret keys between IoT devices and fog nodes to be dynamically generated by incorporating the attribute group keys. Then, the encryption and decryption functions for IoT devices are outsourced to fog nodes, which present the CP-ABE validation.
Incorporating a Topic Model into a Hypergraph Neural Network for Searching-Scenario Oriented Recommendations

The personalized recommendation system is a useful tool adopted by e-retailers to help consumers to find items in line with their preferences. Existing methods focus on learning user preferences from a user-item matrix or online reviews after purchasing, and they ignore the interactive features in the process of users’ learning about product information through search queries before they make a purchase. To this end, this study develops a topic augmented hypergraph neural network framework to predict the user’s purchase intention by connecting the latent topics embedded in a consumer’s online queries to their click, purchase, and online review behavior, which aims at mining the connection information existing in the interaction graph domain. Meanwhile, in order to reduce the influence of text noise words by fusing topic information, we integrate the topic distribution and convolutional embedding to better represent each user and item, which can make up for the lack of topic information in traditional convolutional neural networks. Extensive empirical evaluations on real-world datasets demonstrate that the proposed framework improves the novelty of recommendation items as well as accuracy. From a managerial perspective, recommending diversified and novel items to consumers may increase the users’ satisfaction, which is conducive to the sustainable development of e-commerce enterprises.
TimeStitch: Exploiting Slack to Mitigate Decoherence in Quantum Circuits

Quantum systems have the potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit [ 13 , 20 , 56 ], calibrating new gates [ 50 ], or extending a circuit’s runtime [ 32 ], this article’s primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in Noisy Intermediate Scale Quantum devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This article presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines. Unlike past approaches that apply reversibility properties to improve quantum circuit execution [ 35 ], TimeStitch amplifies fidelity without violating critical path frontiers in either the slack tuning procedures or the final rescheduled circuit. On average, compared to a state-of-the-art baseline, a practically constrained TimeStitch achieves a mean 38% relative improvement in success rates, with a maximum of 106%, while observing bounds on circuit depth. When unconstrained by depth criteria, TimeStitch produces a mean relative fidelity increase of 50% with a maximum of 256%. Finally, when TimeStitch intelligently leverages periodic dynamical decoupling within its scheduling framework, a mean 64% improvement is observed over the baseline, relatively outperforming stand-alone dynamical decoupling by 19%, with a maximum of 287%.
Hand Gesture Controlled Vehicle

Gesture controlled vehicle is a type of robot which can operated by human hand movements. The user needs to wear a glove which consists of transmitter and sensors like IR sensor. These sensors help in recording the movement of hand in a desired direction which ultimately results in the motion of robot or vehicle. It will be very easy for user to interact with car or robot more easily due to wire free connectivity. Vehicle can be controlled with the help of accelerometer sensor which is connected to hand glove. This will help the applicant to control the forward, backward and sideways movements. We have made the cross connections of motors which will help in the movement of tyres so that vehicle can take a sharp turn without any difficulty. Hand gesture controlled driverless vehicle is developed with the help of Arduino Nano, three-axis accelerometer, RF transmitter and receiver module..
IMPROVING INDONESIAN EFL STUDENTS� SPEAKING SKILL THROUGH PECHA KUCHA

EFL students speaking skill at the university level is normally assessed through presentations in the teaching and learning activities. Therefore, the ability to deliver effective presentations is needed as it affects the EFL students competence in speaking. Most EFL students choose to use Microsoft PowerPoint to deliver their presentations in class. However, the additional pressure given to present in English normally causes them to include as much information as possible on their slides. They have the tendency to focus too much on the content and neglect the delivery aspect. As a result, most of the students only end up reading aloud their text-heavy slides, which often leads to boredom. Pecha Kucha is then introduced as a creative and innovative presentation format to improve EFL students speaking skill in delivering presentations. It is a simple, concise and effective presentation format which allows only twenty (20) slides that will advance automatically for 20 seconds each. The results of the study show that the use of Pecha Kucha has increased the EFL students speaking skill in delivering presentations. It is shown from the increased mean scores of the students presentation scores from before and after Pecha Kucha is used. It is also strengthened by the results of the interview on two students stating that Pecha Kucha has improved their speaking skill in delivering effective presentations in terms of the language use, speech delivery and interaction with audience.DOI:doi.org/10.24071/llt.2019.220109
Evidence-based lean conceptual data modelling languages

Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. There is no known DL language that matches exactly the features of thoseprofiles and the common core is small (in the tractable DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.
Eformer: Edge Enhancement based Transformer for Medical Image Denoising

In this work, we present Eformer - Edge enhancement based transformer, a novel architecture that builds an encoder-decoder network using transformer blocks for medical image denoising. Non-overlapping window-based self-attention is used in the transformer block that reduces computational requirements. This work further incorporates learnable Sobel-Feldman operators to enhance edges in the image and propose an effective way to concatenate them in the intermediate layers of our architecture. The experimental analysis is conducted by comparing deterministic learning and residual learning for the task of medical image denoising. To defend the effectiveness of our approach, our model is evaluated on the AAPM-Mayo Clinic Low-Dose CT Grand Challenge Dataset and achieves state-of-the-art performance, $i.e.$, 43.487 PSNR, 0.0067 RMSE, and 0.9861 SSIM. We believe that our work will encourage more research in transformer-based architectures for medical image denoising using residual learning.
An Electro-Photonic System for Accelerating Deep Neural Networks

The number of parameters in deep neural networks (DNNs) is scaling at about 5$\times$ the rate of Moore's Law. To sustain this growth, photonic computing is a promising avenue, as it enables higher throughput in dominant general matrix-matrix multiplication (GEMM) operations in DNNs than their electrical counterpart. However, purely photonic systems face several challenges including lack of photonic memory and accumulation of noise. In this paper, we present an electro-photonic accelerator, ADEPT, which leverages a photonic computing unit for performing GEMM operations, a vectorized digital electronic ASIC for performing non-GEMM operations, and SRAM arrays for storing DNN parameters and activations. In contrast to prior works in photonic DNN accelerators, we adopt a system-level perspective and show that the gains while large are tempered relative to prior expectations. Our goal is to encourage architects to explore photonic technology in a more pragmatic way considering the system as a whole to understand its general applicability in accelerating today's DNNs. Our evaluation shows that ADEPT can provide, on average, 5.73$\times$ higher throughput per Watt compared to the traditional systolic arrays (SAs) in a full-system, and at least 6.8$\times$ and $2.5\times$ better throughput per Watt, compared to state-of-the-art electronic and photonic accelerators, respectively.
Public-Key Cryptography Based on Tropical Circular Matrices

Some public-key cryptosystems based on the tropical semiring have been proposed in recent years because of their increased efficiency, since the multiplication is actually an ordinary addition of numbers and there is no ordinary multiplication of numbers in the tropical semiring. However, most of these tropical cryptosystems have security defects because they adopt a public matrix to construct commutative semirings. This paper proposes new public-key cryptosystems based on tropical circular matrices. The security of the cryptosystems relies on the NP-hard problem of solving tropical nonlinear systems of integers. Since the used commutative semiring of circular matrices cannot be expressed by a known matrix, the cryptosystems can resist KU attacks. There is no tropical matrix addition operation in the cryptosystem, and it can resist RM attacks. The new cryptosystems can be considered as a potential post-quantum cryptosystem.
Sangrahaka: A Tool for Annotating and Querying Knowledge Graphs

In this work, we present a web-based annotation and querying tool Sangrahaka. It annotates entities and relationships from text corpora and constructs a knowledge graph (KG). The KG is queried using templatized natural language queries. The application is language and corpus agnostic, but can be tuned for special needs of a specific language or a corpus. A customized version of the framework has been used in two annotation tasks. The application is available for download and installation. Besides having a user-friendly interface, it is fast, supports customization, and is fault tolerant on both client and server side. The code is available at https://github.com/hrishikeshrt/sangrahaka and the presentation with a demo is available at https://youtu.be/nw9GFLVZMMo.
FNet: Mixing Tokens with Fourier Transforms

We show that Transformer encoder architectures can be sped up, with limited accuracy costs, by replacing the self-attention sublayers with simple linear transformations that "mix" input tokens. These linear mixers, along with standard nonlinearities in feed-forward layers, prove competent at modeling semantic relationships in several text classification tasks. Most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92-97% of the accuracy of BERT counterparts on the GLUE benchmark, but trains 80% faster on GPUs and 70% faster on TPUs at standard 512 input lengths. At longer input lengths, our FNet model is significantly faster: when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs). Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes; for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Roadmap for Rare-earth Quantum Computing

Several platforms are being considered as hardware for quantum technologies. For quantum computing (QC), superconducting qubits and artificially trapped ions are among the leading platforms, but many others also show promise, e.g. photons, cold atoms, defect centers including Rare-Earth (RE) ions. So far, results are limited to the regime of noisy intermediate scale qubits (NISQ), with a small number of qubits and a limited connectivity, and it is likely that future QC hardware will utilize several existing platforms in different ways. Thus, it currently makes sense to invest resources broadly and explore the full range of promising routes to quantum technology. Rare-earth ions in solids constitute one of the most versatile platforms for future quantum technology. One advantage is good coherence properties even when confined in strong natural traps inside a solid-state matrix. This confinement allows very high qubit densities and correspondingly strong ion-ion couplings. In addition, although their fluorescence is generally weak, cavity integration can enhance the emission greatly and enable very good connections to photonic circuits, including at the telecom wavelengths, making them promising systems for long-term scalability. The primary aim of this roadmap is to provide a complete picture of what components a RE quantum computer would consist of, to describe the details of all parts required to achieve a scalable system, and to discuss the most promising paths to reach it. In brief, we find that clusters of 50-100 single RE ions can act as high fidelity qubits in small processors, occupying only about (10 nm)^3. Due to the high capacity for integration of the RE systems, they be optically read out and connected to other such clusters for larger scalability. We make suggestions for future improvements, which could allow the REQC platform to be a leading one.
Leveraging cross-platform data to improve automated hate speech detection

Hate speech is increasingly prevalent online, and its negative outcomes include increased prejudice, extremism, and even offline hate crime. Automatic detection of online hate speech can help us to better understand these impacts. However, while the field has recently progressed through advances in natural language processing, challenges still remain. In particular, most existing approaches for hate speech detection focus on a single social media platform in isolation. This limits both the use of these models and their validity, as the nature of language varies from platform to platform. Here we propose a new cross-platform approach to detect hate speech which leverages multiple datasets and classification models from different platforms and trains a superlearner that can combine existing and novel training data to improve detection and increase model applicability. We demonstrate how this approach outperforms existing models, and achieves good performance when tested on messages from novel social media platforms not included in the original training data.