Reviewerly/exHarmony huggingface dataset:
MONITORING STUDENT ATTENDANCE USING A SMART SYSTEM AT TAIF UNIVERSITY

The university system in the Kingdom of Saudi Arabia is concerned with student attendance for lectures, and it is the responsibility of lecturers to monitor student attendance for each lecture. By the end of the semester, students get an attendance register indicating which lectures the student has attended and it reports the calculated percentage for each student’s attendance in each course. Universities have regulated the mechanisms and the acceptable percentages of student absence. The process for a lecturer to manually check student attendance consumes a lot of time and effort, either during the lecture or when in the process of emptying absenteeism and inserting it into the university’s electronic system. Therefore, Saudi universities compete to find modern methods of checking student attendance that will avoid the disadvantages of manually taking attendance. For this reason, they have produced electronic attendance systems, for example, using a student's fingerprint, an eye recognition system, or a mobile phone system to read a QR code designed for the same purpose. All of these systems have the disadvantage that they consume a lot of time, as all students have to line up at the fingerprint reader or the eye detector for identification. Therefore, the problem of the consumption of lecture time is still present, even with these modern systems. Therefore, the aim of this research is to propose a smart mobile application that is able to check the attendance of students without having to consume lecture time or require any effort from the lecturer. The system automatically recognizes the attendance of students through their university ID cards. Each lecturer would use his/ her own mobile phone to use the proposed system to check the attendance of students instead of using manual method to register the attendance of students and the students’ ID cards that are detected by coming within range of the lecturer reader would represent present students, and missing student ID cards represent absent students
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy

Identifying the hypernym relations that hold between words is a fundamental task in NLP. Word embedding methods have recently shown some capability to encode hypernymy. However, such methods tend not to explicitly encode the hypernym hierarchy that exists between words. In this paper, we propose a method to learn a hierarchical word embedding in a specii¬c order to capture the hypernymy. To learn the word embeddings, the proposed method considers not only the hypernym relations that exists between words on a taxonomy, but also their contextual information in a large text corpus. The experimental results on a supervised hypernymy detection and a newly-proposed hierarchical path completion tasks show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning word and hypernym-specii¬c word embeddings on multiple benchmarks.
Generalized Boundary Detection Using Compression-based Analytics

We present a new method for boundary detection within sequential data using compression-based analytics. Our approach is to approximate the information distance between two adjacent sliding windows within the sequence. Large values in the distance metric are indicative of boundary locations. A new algorithm is developed, referred to as sliding information distance (SLID), that provides a fast, accurate, and robust approximation to the normalized information distance. A modified smoothed z-score algorithm is used to locate peaks in the distance metric, indicating boundary locations. A variety of data sources are considered, including text and audio, to demonstrate the efficacy of our approach.
SPFEMD: Super-pixel Based Finger Earth Mover’s Distance for Hand Gesture Recognition

In this paper, we propose super-pixel based finger earth mover's distance (SPFEMD) for hand gesture recognition. For finger representation, we design SPFEMD for similarity measurement between fingers and hand gestures, and use it as the distance metric for hand gesture recognition. First, we extract hands without any user interaction using both color and depth from Kinect camera. Then, we obtain the seed for hand segmentation on the depth map and segment hand on the color image using the seed. Since fingers contain distinct features for hand gesture recognition, we decompose the hand segment into palm and fingers based on morphological operation. Finally, we perform hand gesture recognition from fingers based on SPFEMD. Experiments on publicly available and our own data sets show the superiority of the proposed method over state-of-the-arts in terms of accuracy and confusion matrices.
An investigation into the output tax consequences of bitcoin transactions for a South African value-added tax vendor

Background: The use of bitcoin in South Africa is fairly new, but has increased as several online retailers now accept bitcoin as a means of payment. The South African Revenue Service has released a media statement regarding the normal tax treatment of cryptocurrencies (such as bitcoin), but policy regarding the value-added tax (VAT) treatment of cryptocurrencies is still pending. Aim: The objective of the study is to determine the output tax consequences for a South African VAT vendor who receives bitcoins in exchange for the supply of goods or services that are subject to VAT, and when the same South African VAT vendor exchanges the bitcoins for South African rand at a local exchange platform. Setting: This article examines existing literature in a South African VAT environment. Method: A non-empirical study based on existing literature is performed. Results: It is found that when interpreting the (current) VAT Act No. 89 of 1991, the receiving of bitcoin in exchange for the supply of goods or services, as well as the exchange of bitcoin for South African rand, is subject to output tax at the standard rate of 14%, which will lead to ‘double taxation’. Conclusion: It was shown through this study that the proposed treatment as explained in the previous section would impose ‘double taxation’.
Geo-Portal Implementation with a Combined Approach of AHP and SWOT

In order to achieve the goals of sustainable development, reliable and more quality spatial data is required in the decision-making and planning process. In other words, the existence of an appropriate infrastructure of spatial data is necessary for the sustainable development of the community. Recently, the creation of a Spatial Data Infrastructure (SDI) is in the program of many states at various national, provincial and local levels. SDI can be defined from a variety of perspectives; however, the geoportal design and implementation play a key role as a platform for providing spatial services. In this regard, various software exists as a geoportal platform, which is a necessity to choose an appropriate software tool among them. To achieve this goal, a set of related software products should be investigated and compared. Then, the criteria that are important from the perspective of the user and the developer are selected for software comparison, and ultimately software packages which support SDI (for geo-portal) are selected. In this regard, attempts to select one software with emphasis on environmental activities among four open source software for geo-portal implementation. In this article, the combination of AHP and SWOT is used to achieve this goal with an emphasis on environmental activities. Therefore, the criteria are first weighed using the AHP method, then, the SWOT method is used to select the appropriate software based on identifying strengths, weaknesses, opportunities, and threats.
Reversible Data Hiding Technique using Novel Interpolation Technique and Discrete Cosine Transform

The application of internet has expanded colossally in recent years. Subsequently, security of data has turned out to be a quite important factor to focus. It is likewise discernible that over the internet, data can be controlled, cautioned or compromised. To overcome these issues numerous methodologies are proposed. These methods depend on some type of mechanism, where data is encoded and with the help of a key message and using the key it can be recovered the receiver. In pictures, reversible data hiding (RDH) is a strategy which is exceptionally useful in regaining original cover after the embedded message is separated. This key strategy is comprehensively utilized as a piece of medical imagery, law crime scene investigation and military symbolism, where no distortion of the cover is allowed. Because of being reversible, RDH has pulled in great research interest. This paper discusses an improvement in RDH technique while also considering DCT based compression technique for further improvements. Simulation has been carried out to evaluate the performance in terms of Peak-to-Signal Noise Ratio (PSNR) and Structural Similarity Index (SSIM). It has been found that our proposed method performs better than earlier methods.
A Modular Deep Learning Approach for Extreme Multi-label Text Classification.

Extreme multi-label classification (XMC) aims to assign to an instance themost relevant subset of labels from a colossal label set. Due to modernapplications that lead to massive label sets, the scalability of XMC hasattracted much recent attention from both academia and industry. In this paper,we establish a three-stage framework to solve XMC efficiently, which includes1) indexing the labels, 2) matching the instance to the relevant indices, and3) ranking the labels from the relevant indices. This framework unifies manyexisting XMC approaches. Based on this framework, we propose a modular deeplearning approach SLINMER: Semantic Label Indexing, Neural Matching, andEfficient Ranking. The label indexing stage of SLINMER can adopt differentsemantic label representations leading to different configurations of SLINMER.Empirically, we demonstrate that several individual configurations of SLINMERachieve superior performance than the state-of-the-art XMC approaches onseveral benchmark datasets. Moreover, by ensembling those configurations,SLINMER can achieve even better results. In particular, on a Wiki dataset witharound 0.5 millions of labels, the precision@1 is increased from 61% to 67%.
Be Wary of Black-Box Trading Algorithms

Black-box algorithms now account for nearly a third of all US stock trades. It is a mistake to think that these algorithms possess superhuman intelligence. In reality, computers do not have the common sense and wisdom that humans have accumulated by living. Trading algorithms are particularly dangerous because they are so efficient at discovering statistical patterns—but so utterly useless in judging whether the discovered patterns are meaningful. <b>TOPICS:</b>Quantitative methods, risk management
Hyperelliptic systems of sequences of rank 4

Abstract Sequences of complex numbers satisfying functional relations of bilinear type are investigated. The results obtained are used in describing all 1-periodic entire functions <?CDATA $f\colon \mathbb C\to\mathbb C$?> such that the expansion <?CDATA ${f(x+y)f(x-y)}=\varphi_1(x)\psi_1(y)+\dots+\varphi_4(x)\psi_4(y)$?> holds for some <?CDATA $\varphi_j,\psi_j\colon \mathbb C\to\mathbb C$?> . Bibliography: 38 titles.
Fringe-Distortion-Correction for Polarized Low-Coherence Interferometry With Phosphor-Based LED

An interferogram distortion correction method is proposed for polarized low-coherence interferometry with the phosphor-based LED. A mathematical model of the interference fringes with distortion was established to analyze the distortion influence caused by the combined influence of the dual Gaussian-like peak spectral phosphor-based LED and the location-related birefringence dispersion of the polarization low coherence interference system. With the distortion model, the proposed method corrected the mismatch of the envelope peak and the phase peak based on the central wavenumber of single Gaussian-like peak spectrum, and the interference fringes envelope offset of dual Gaussian-like peak spectrum. A pressure experiment was carried out to verify the feasibility of the proposed method. According to the experiment result, the interferogram distortion was corrected effectively, the precision of demodulation was improved, and the demodulation error was less than 0.06 kPa in the pressure measurement range of 230 kPa. The interference fringes intensity was also increased by 10% compared to the original intensity.
Illumination-Guided Video Composition via Gradient Consistency Optimization

Video composition aims at cloning a patch from the source video into the target scene to create a seamless and harmonious blending frame sequence. Previous work in video composition usually suffers from artifacts around the blending region and spatial-temporal consistency when illumination intensity varies in the input source and target video. We propose an illumination-guided video composition method via a unified spatial and temporal optimization framework. Our method can produce globally consistent composition results and maintain the temporal coherency. We first compute a spatial-temporal blending boundary iteratively. For each frame, the gradient field of the target and source frames are mixed adaptively based on gradients and inter-frame color difference. The temporal consistency is further obtained by optimizing luminance gradients throughout all the composition frames. Moreover, we extend the mean-value cloning by smoothing discrepancies between the source and target frames, then eliminate the color distribution overflow exponentially to reduce falsely blending pixels. Various experiments have shown the effectiveness and high-quality performance of our illumination-guided composition.
Dataflow-Based Joint Quantization for Deep Neural Networks

This paper addresses a challenging problem – how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage. In order to alleviate the computation and storage burdens, we propose a novel dataflow-based joint quantization approach with the hypothesis that a fewer number of quantization operations would incur less information loss and thus improve the final performance. It first introduces a quantization scheme with efficient bit-shifting and rounding operations to represent network parameters and activations in low precision. Then it re-structures the network architectures to form unified modules for optimization on the quantized model. Extensive experiments on ImageNet and KITTI validate the effectiveness of our model, demonstrating that state-of-the-art results for various tasks can be achieved by this quantized model. Besides, we designed and synthesized an RTL model to measure the hardware costs among various quantization methods. For each quantization operation, it reduces area cost by about 15 times and energy consumption by about 9 times, compared to a strong baseline.
Quantum algorithm for simulating an experiment: Light interference from single ions and their mirror images

We widen the range of applications for quantum computing by introducing digital quantum simulation methods for coherent light-matter interactions: We simulate an experiment where the emitted light from a single ion was interfering with its mirror image [Eschner et. al., Nature 431, 495 (2001)]. Using the quantum simulation software q1tsim we accurately reproduce the interference pattern which had been observed experimentally and also show the effect of the mirror position on the spontaneous emission rate of the ion. In order to minimize the number of required qubits we implement a qubit-reinitialization technique. We show that a digital quantum simulation of complex experiments in atomic and quantum physics is feasible with no more than seven qubits, a setting which is well within reach for advanced quantum computing platforms.
Compensation of polarization-dependent loss using noiseless amplification and attenuation

Polarization-dependent loss (PDL) is a serious problem that hinders the transfer of polarization qubits through quantum networks. Recently it has been shown that the detrimental effects of PDL on qubit fidelity can be compensated for with the introduction of an additional passive PDL element that rebalances the polarization modes of the transmitted qubit. This procedure works extremely well when the output of the system is postselected on photon detection. However, in cases where the qubit might be needed for further analysis this procedure introduces unwanted vacuum terms into the state. Here we present procedures for the compensation of the effects of PDL using noiseless amplification and attenuation. Each of these techniques introduces a heralding signal into the correction procedure that significantly reduces the vacuum terms in the final state. When detector inefficiency and dark counts are included in the analysis noiseless amplification remains superior, in terms of the fidelity of the final state, to both noiseless attenuation and passive PDL compensation for detector efficiencies greater than 40%.
Post-quantum Provably-Secure Authentication and MAC from Mersenne Primes

This paper presents a novel, yet efficient secret-key authentication and MAC, which provide post-quantum security promise, whose security is reduced to the quantum-safe conjectured hardness of Mersenne Low Hamming Combination ($$\mathsf {MERS}$$) assumption recently introduced by Aggarwal, Joux, Prakash, and Santha (CRYPTO 2018). Our protocols are very suitable to weak devices like smart card and RFID tags.
Quantum computation by teleportation and symmetry

A preliminary overview of measurement-based quantum computation in the setting of symmetry and topological phases of quantum matter is given. The underlying mechanism for universal quantum computation by teleportation or symmetry are analyzed, with the emphasis on the relation with tensor-network states in the presence of various symmetries. Perspectives are also given for the role of symmetry and phases of quantum matter in measurement-based quantum computation and fault tolerance.
DAGS: Reloaded Revisiting Dyadic Key Encapsulation

In this paper we revisit some of the main aspects of the DAGS Key Encapsulation Mechanism, one of the code-based candidates to NIST’s standardization call for the key exchange/encryption functionalities. In particular, we modify the algorithms for key generation, encapsulation and decapsulation to fit an alternative KEM framework, and we present a new set of parameters that use binary codes. We discuss advantages and disadvantages for each of the variants proposed.
A scalable mobile context-aware recommender system for a smart city administration

With the innovation of new Information and Communication Technologies and the needs of information and knowledge sharing among the city, a smart city system aims to improve it's citizens life quality by offering a set of public services. Within this context, this research work proposes a smart city approach for using these technologies in public services delivery to allow real-time interactions with citizens; we propose a service providing framework based on context-aware recommendation approach to improve the city's digital services according to citizen’s context and the backend as a service (BAAS) approach on cloud to enhance the scalability of the system in large workloads. In this study, we evaluated the scalability and interactivity of the proposed approach by measuring response time and exchanged data metrics. To estimate the scalability, we performed series of load tests where response time values increased with the addition of requests in different cases but remained acceptable. In terms of interactivity, the exchanged data between the citizen application and the city’s backend was measured at maximum by 117 Kb/s. The experiments revealed that context-aware recommendation approach optimised interactions by reducing the amount of exchanged data, and the BAAS approach improved the scalability of the system and allowed handling concurrent requests.
X-BERT: eXtreme Multi-label Text Classification with BERT

Extreme multi-label text classification (XMC) aims to tag each input text with the most relevant labels from an extremely large label set, such as those that arise in product categorization and e-commerce recommendation. Recently, pretrained language representation models such as BERT achieve remarkable state-of-the-art performance across a wide range of NLP tasks including sentence classification among small label sets (typically fewer than thousands). Indeed, there are several challenges in applying BERT to the XMC problem. The main challenges are: (i) the difficulty of capturing dependencies and correlations among labels, whose features may come from heterogeneous sources, and (ii) the tractability to scale to the extreme label setting as the model size can be very large and scale linearly with the size of the output space. To overcome these challenges, we propose X-BERT, the first feasible attempt to finetune BERT models for a scalable solution to the XMC problem. Specifically, X-BERT leverages both the label and document text to build label representations, which induces semantic label clusters in order to better model label dependencies. At the heart of X-BERT is finetuning BERT models to capture the contextual relations between input text and the induced label clusters. Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model. Empirically, on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67:80%, a substantial improvement over 32.58%/60.91% of deep learning baseline fastText and competing XMC approach Parabel, respectively. This amounts to a 11.31% relative improvement over Parabel, which is indeed significant since the recent approach SLICE only has 5.53% relative improvement.
Nature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction

Fast and invariant feature extraction is crucial in certain computer vision applications where the computation time is constrained in both training and testing phases of the classifier. In this paper, we propose a nature-inspired dimensionality reduction technique for fast and invariant visual feature extraction. The human brain can exchange the spatial and spectral resolution to reconstruct missing colors in visual perception. The phenomenon is widely used in the printing industry to reduce the number of colors used to print, through a technique, called color dithering. In this work, we adopt a fast error-diffusion color dithering algorithm to reduce the spectral resolution and extract salient features by employing novel Hessian matrix analysis technique, which is then described by a spatial-chromatic histogram. The computation time, descriptor dimensionality and classification performance of the proposed feature are assessed under drastic variances in orientation, viewing angle and illumination of objects comparing with several different state-of-the-art handcrafted and deep-learned features. Extensive experiments on two publicly available object datasets, coil-100 and ALOI carried on both a desktop PC and a Raspberry Pi device show multiple advantages of using the proposed approach, such as the lower computation time, high robustness, and comparable classification accuracy under weakly supervised environment. Further, it showed the capability of operating solely inside a conventional SoC device utilizing a small fraction of the available hardware resources.