Reviewerly/exHarmony huggingface dataset:
Comment on “Practical quantum key distribution with geometrically uniform states”

In a recent paper, Kravtsov and Molotkov [Phys. Rev. A 100, 042329 (2019)] propose a quantum key distribution protocol and claim ``the protocol security is analyzed by an explicit construction of the most optimal unitary attack'' and ``we provide an extensive security analysis of the proposed protocol by considering a general symmetric unitary attack, and give succinct relations for the asymptotic secret key rate in the case of $N=8$.'' We show that these claims are erroneous and propose two attacks which are more effective than the attacks considered in the paper of Kravtsov and Molotkov.
Controlling the Bomb Disposal Robot using Microsoft Kinect Sensor

This study aims to control the bomb disposal robot by developing a design and creating an intelligence using Microsoft Kinect controller. This also shows the comparison of speed in terms of time between the joystick controller and the Microsoft Kinect controller. The proponents analyzed the design and mechanism of the bomb disposal robot and implemented a dual arm robot with 10 Degrees of Freedom, they develop a counter torque and reduce the load that the servo motor experience. To create an intelligence, the proponents get the needed Microsoft Kinect joints, constructed the vectors and acquired the angles from those vectors using dot product. For the proponents to evaluate the speed of Microsoft Kinect controller, they assess the capability of the Microsoft Kinect controlled robot and perform all 23 tasks the joystick-controlled robot did and made 30 test runs with each task it completed. They tally the time in finishing the task, get the averages and the time differences between the two controllers. Furthermore, the proponents asserted that the design for the bomb disposal robot has been implemented as well as the algorithm in creating the intelligence but, the proponents observed that the robot arms can only lift light objects and the wheels can only maneuver in flat surfaces. Similarly, the proponents analyzed that the Microsoft Kinect controller is faster in completing the tasks than the joystick controller in the tasks that both controllers were able to complete.
Neurosurgery planning based on automated image recognition and optimal path design

Abstract Stereotactic neurosurgery requires a careful planning of cannulae paths to spare eloquent areas of the brain that, if damaged, will result in loss of essential neurological function such as sensory processing, linguistic ability, vision, or motor function. We present an approach based on modelling, simulation, and optimization to set up a computational assistant tool. Thereby, we focus on the modeling of the brain topology, where we construct ellipsoidal approximations of voxel clouds based on processed MRI data. The outcome is integrated in a path-planning problem either via constraints or by penalization terms in the objective function. The surgical planning problem with obstacle avoidance is solved for different types of stereotactic cannulae using numerical simulations. We illustrate our method with a case study using real MRI data.
A Systematic Literature Review of Hausa Natural Language Processing

The processing of natural languages is an area of computer science that has gained growing attention recently. NLP helps computers recognize, in other words, the ways in which people use their language. NLP research, however, has been performed predominantly on languages with abundant quantities of annotated data, such as English, French, German and Arabic. While the Hausa Language is Africa's second most commonly used language, only a few studies have so far focused on Hausa Natural Language Processing (HNLP). In this research paper, using a keyword index and article title search, we present a systematic analysis of the current literature applicable to HNLP in the Google Scholar database from 2015 to June 2020. A very few research papers on HNLP research, especially in areas such as part-of-speech tagging (POS), Name Entity Recognition (NER), Words Embedding, Speech Recognition and Machine Translation, have just recently been released. This is due to the fact that for training intelligent models, NLP depends on a huge amount of human-annotated data. HNLP is now attracting researchers' attention after extensive research on NLP in English and other languages has been performed. The key objectives of this paper are to promote research, to define likely areas for future studies in the HNLP, and to assist in the creation of further examinations by researchers for relevant studies.
Efficient High-Dimensional Kernel k-Means++ with Random Projection

Using random projection, a method to speed up both kernel k-means and centroid initialization with k-means++ is proposed. We approximate the kernel matrix and distances in a lower-dimensional space Rd before the kernel k-means clustering motivated by upper error bounds. With random projections, previous work on bounds for dot products and an improved bound for kernel methods are considered for kernel k-means. The complexities for both kernel k-means with Lloyd’s algorithm and centroid initialization with k-means++ are known to be O(nkD) and Θ(nkD), respectively, with n being the number of data points, the dimensionality of input feature vectors D and the number of clusters k. The proposed method reduces the computational complexity for the kernel computation of kernel k-means from O(n2D) to O(n2d) and the subsequent computation for k-means with Lloyd’s algorithm and centroid initialization from O(nkD) to O(nkd). Our experiments demonstrate that the speed-up of the clustering method with reduced dimensionality d=200 is 2 to 26 times with very little performance degradation (less than one percent) in general.
Particle Swarm Optimization Algorithm for Detecting Distributed Predicates

Metaheuristic algorithms are widely used to solve NP-complete problems in several domains. Distributed predicates detection is a fundamental distributed systems problem that has many useful applications. The problem of distributed predicates detection, in general, is known to be an NP-complete problem. In this paper, we developed a detection algorithm inspired by the particle swarm optimization algorithm, one of the well-known metaheuristic algorithms applied to solve problems in several domains. The proposed detection algorithm deal with distributed predicates under the possibly modality. We compared the performance of the proposed distributed predicates detection algorithm with several other detection algorithms. The experimental results reveal the effectiveness of the suggested distributed predicates detection algorithm.
SelfWatts: On-the-fly Selection of Performance Events to Optimize Software-defined Power Meters

Fine-grained power monitoring of software-defined infrastructures is unavoidable to maximize the power usage efficiency of data centers. However, the design of the underlying power models that estimate the power consumption of the monitored software components keeps being a long and fragile process that remains tightly coupled to the host machine and prevents a wider adoption by the industry beyond the rich literature on this topic. To overcome these limitations, this paper introduces SELFWATTS: a lightweight power monitoring system that explores and selects the relevant performance events to automatically optimize the power models to the underlying architecture. Unlike state-of-the-art techniques, SELFWATTS does not require any a priori training phase or specific hardware to configure the power models and can be deployed on a wide range of machines, including heterogeneous environments.
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing

Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT. These models are built on the top of transformers, self-supervised learning and transfer learning. Transformed-based PTLMs learn universal language representations from large volumes of text data using self-supervised learning and transfer this knowledge to downstream tasks. These models provide good background knowledge to downstream tasks which avoids training of downstream models from scratch. In this comprehensive survey paper, we initially give a brief overview of self-supervised learning. Next, we explain various core concepts like pretraining, pretraining methods, pretraining tasks, embeddings and downstream adaptation methods. Next, we present a new taxonomy of T-PTLMs and then give brief overview of various benchmarks including both intrinsic and extrinsic. We present a summary of various useful libraries to work with T-PTLMs. Finally, we highlight some of the future research directions which will further improve these models. We strongly believe that this comprehensive survey paper will serve as a good reference to learn the core concepts as well as to stay updated with the recent happenings in T-PTLMs.
Cryptanalysis and improvements of an efficient certificate-based proxy signature scheme for IIoT environments

Recently, Verma et al. (2020) [10] proposed the first pairing-free certificate-based proxy signature (PFCBPS) scheme for IIoT environments. They defined two kinds of adversaries in the PFCBPS scheme and proved that their construction was secure against Type I and Type II adversaries under a standard cryptographic assumption in the random oracle model. In this work, we revisit adversaries' abilities defined by Verma et al. and show that the PFCBPS scheme cannot resist the signature forgery attack performed by a malicious proxy signer (a weak Type II adversary). As a result, the above signature scheme cannot be deployed in practical IIoT applications. Moreover, we discuss the reason for such an attack and provide an improved scheme PFCBPS+.
Analysis User Interface: Mobile Application to Blended Learning Model

This research is concerned with the use of development applications using mobile devices as a tool for blended learning models. However, in developing learning applications, one important concern is the user interface (UI). Therefore, the purpose of this research is to find out which UI design components are easy to use according to the user to be more user friendly, so that the purpose of using mobile applications for education can be realized. This study covers design principles that are appropriate for applications developed for platforms. Based on research, user interface testing has been carried out from 5 user interface principles and analysis using the System Usage Scale (SUS) given to 32 respondents. The results showed that the majority of respondents agreed that the mobile application developed had met the requirements of the user interface element.
Information System Construction and Prediction Model of Leaving Using Big Data Mining and Random Forest

Abstract University talents play an important role in building the core competitiveness of universities and play a decisive role in improving the quality of university education. With the opening up and progress of society, the flow of talents in colleges and universities has become more and more frequent. Therefore, more and more attention has been paid to the analysis of factors affecting the turnover of outstanding college teachers. This paper establishes a random forest model to find out the key factors affecting the turnover of outstanding teachers in colleges and universities, and integrates these variables into an index to help colleges and universities understand which outstanding teachers need to be focused and predict the outstanding teachers on the job, and judge the probability of their departure., Which can formulate effective talent retention measures for the university managers. The experimental results verify that the work itself, work pressure and welfare remuneration are the most important factors affecting the turnover tendency of college teachers, followed by leadership management and development prospects. Relatively speaking, the impact of interpersonal relationships is the least. The evaluation model of university teachers’ turnover intention based on the random forest model proposed in this paper can be further applied to the study of teacher turnover intention.
A chaos theory inspired, asynchronous two-way encryption mechanism for cloud computing

Data exchange over the Internet and other access channels is on the rise, leads to the insecurity of consequences. Many experiments have been conducted to investigate time-efficient and high-randomized encryption methods for the data. The latest studies, however, have still been debated because of different factors. The study outcomes do not yield completely random keys for encryption methods that are longer than this. Prominent repetition makes the processes predictable and susceptible to assaults. Furthermore, recently generated keys need recent algorithms to run at a high volume of transactional data successfully. In this article, the proposed solutions to these two critical issues are presented. In the beginning, one must use the chaotic series of events for generating keys is sufficient to obtain a high degree of randomness. Moreover, this work also proposes a novel and non-traditional validation test to determine the true randomness of the keys produced from a correlation algorithm. An approximate 100% probability of the vital phase over almost infinitely long-time intervals minimizes the algorithms’ complexity for the higher volume of data security. It is suggested that these algorithms are mainly intended for cloud-based transactions. Data volume is potentially higher and extremely changeable 3% to 4% of the improvement in data transmission time with suggested algorithms. This research has the potential to improve communication systems over ten years by unblocking decades-long bottlenecks.
Taking the trumpet up there: enactment of embodied high pitch in a multimodal body schema

Abstract This contribution examines how an expert musician teaches high pitch as an embodied practice in a digital instruction video. Musical meaning-making in this perspective calls for a naturalized phenomenology which deals with the practice of music teaching, which involves a performing body. The notion of high musical pitch in terms of an abstract embodied image schema is challenged in favor of a multidimensional body schema, conceptualized at the interface between multimodal language, i.e. in speech and gesture, and the affordances imposed on musical production by the human body and the instrument artefact. As a result, the traditional metaphorical take on upward verticality, movement and causal force in image schemata becomes a conceptual background which may lead to errors on behalf of the potential student, and needs to be further enriched by natural local corporeal dimensions: immobility, non-vertical change in the lips, mouth and air flow. Such body schemata can be used in teaching more dynamic concepts about enactive knowledge in the body in interactive contexts of knowledge transmission.
ICT uses in education. Theoretical framework proposal for its characterization and analysis

It is clear that during the last few decades, ICT use has considerably increased in education, and it has intensified over the last year as a consequence of an online approach because of the health emergency caused by COVID-19. Nevertheless, despite of the efforts made by schools for keeping updated on the technological equipment and the multiple teacher training processes on this subject, results has not been as expected. This is demonstrated by the higher grade-failure and school drop-out. In this regard, this work aims to propose a theoretical framework to guide characterization and analysis processes on the use of ICT in education. The above, form Vygotsky’s sociocultural theory of human learning, which favors the social construction of learning, but also, from Edgar Morán’s complex thought theory, who prioritizes pertinent thought for social problem solving. And, finally, from online education psychology, where Cesar Coll and Carles Monereo stresses the importance of building working environments that promotes meaningful learning. This work aims to offer a reference framework for the case study, for implementation of improvement processes in favor of the students.
A cost‐efficient resemblance detection scheme for post‐deduplication delta compression in backup systems

Summary Delta compression, which is efficient in removing repeated string among similar chunks, can be used as a complement to data deduplication in backup storage for extra space savings. The process of detecting similar candidates to use as the base for delta compression is called resemblance detection. Several indexes are required for resemblance detection. Maintaining them in RAM would limit the system scalability and increase system cost. Storing them on the disk suffers from low throughput due to poor random I/O performance of the disk. In this article, we present the history‐aware resemblance detection (HARD), a cost‐efficient resemblance detection approach that captures most of the similar chunks with a limited memory footprint. HARD is based on the observation that, for chunks in a backup, most of their similar chunks can be found in the most recent backups. HARD thus only indexes super‐features in the most recent backups for resemblance detection to reduce the memory footprint of resemblance indexes while captures most of the potential similar chunks for delta compression. Experimental results based on three real‐world datasets show that HARD achieves higher compression than the state‐of‐the‐art approach.
Internet of Things e-health revolution: secured transmission of homeopathic e-medicines through chaotic key formation

In this generation of advanced Internet of Things-based e-health systems, medical data communication is a significant component of concern. Dementia, anxiety, schizophrenia, depression, major depressive disorder, etc. are the most common mental complications. In this chapter, a chaotic symmetric key of 128 bits has been generated by the key distribution center (KDC). Homeopathy physicians prescribe e-prescriptions to treat patients after online symptoms analysis. This e-prescription is encrypted through the session key that has been received from KDC. Secret sharing on the predefined number of users in the homeopathy telemedicine system is incorporated. The reverse operation is done to decrypt the e-prescription after mixing the threshold shares. Several statistics-related tests were performed on the proposed technique for its validation. Thus, homeopathic secured online treatment is useful in treating mentally challenged patients (noninvasive and noncritical) by eliminating the geographical barriers.
Determining the Robustness of Privacy Enhancing DeID Against the ReID Adversary: An Experimental Study

Prior research literature shows that there has been considerable work done in the last decade in the area of image de-identification (DeID) for privacy protection. With the advances made in privacy enhancing image DeID techniques, there have been research studies on different DeID performance evaluation approaches for determining the effectiveness of these methods. Existing approaches for evaluating DeID methods can be classified into three separate categories - analysis of privacy versus utility, analysis of viewer experience-based user studies, and analysis of robustness against adversarial attacks. However, none of these categorized approaches have utilized person re-identification (ReID) for evaluating DeID. Additionally, there are no previous research studies that have analyzed the threat of ReID to DeID. In this paper, we present a unique experimental case study that demonstrates how ReID can be used successfully for evaluating the efficacy of DeID techniques, and how, in the process, we can assess the threat of ReID to DeID. We describe a novel approach, in which a selected ReID algorithm is pitted against multiple DeID techniques to test the robustness of these DeID methods, and to determine if ReID can pose a threat to DeID as an adversary. Through this approach, we compare the DeID performances based upon how effectively they can deter successful ReID in the privacy enhanced versions of the ReID image dataset. Our preliminary results show how we can potentially evaluate DeID and compare DeID performances by analyzing the extents to which they are able to successfully resist re-identification i.e., by studying the impact of DeID on the ReID performances.
Improving Generalization of Batch Whitening by Convolutional Unit Optimization

Batch Whitening is a technique that accelerates and stabilizes training by transforming input features to have a zero mean (Centering) and a unit variance (Scaling), and by removing linear correlation between channels (Decorrelation). In commonly used structures, which are empirically optimized with Batch Normalization, the normalization layer appears between convolution and activation function. Following Batch Whitening studies have employed the same structure without further analysis; even Batch Whitening was analyzed on the premise that the input of a linear layer is whitened. To bridge the gap, we propose a new Convolutional Unit that in line with the theory, and our method generally improves the performance of Batch Whitening. Moreover, we show the inefficacy of the original Convolutional Unit by investigating rank and correlation of features. As our method is employable off-the-shelf whitening modules, we use Iterative Normalization (IterNorm), the state-of-the-art whitening module, and obtain significantly improved performance on five image classification datasets: CIFAR-10, CIFAR-100, CUB-200-2011, Stanford Dogs, and ImageNet. Notably, we verify that our method improves stability and performance of whitening when using large learning rate, group size, and iteration number. Code is available at https://github.com/YooshinCho/pytorch_ConvUnitOptimization.
Evaluating Named Entities Recognition (NER) tools vs algorithms adapted to the extraction of locations

Named-entity Recognition (NER) is an important area in the field of text information extraction. In recent years, several NER tools have emerged, each with its own methodologies to identify different entities and perform in several languages. These circumstances make it difficult for the user to determine the right tool for a specific application. Thus, it was proposed to evaluate NER technique via the SpaCy library, and an algorithm adapted to entity extraction (Levenshtein's Algorithm). This evaluation focused on the identification of location tags in Spanish-language texts. Based on the results, the appropriate method and the linked libraries or algorithms were obtained. This allowed to determine that, for this domain, the technique that presented the best metrics performance was SpaCy.