Reviewerly/exHarmony huggingface dataset:
Statistical Approach to select the Best Suitable Solar Model for Global Radiation: Case Study of Karachi, Pakistan

Utilization of solar energy for the fulfillment of daily routine tasks has gained a lot of interest in the recent past. The complete utilization can be accomplished only if proper solar data is available for a particular site. The measurement of solar data is expensive and time consuming. Different empirical correlations are available for the estimation of solar radiations on a particular site. In this research, monthly average daily solar radiation on horizontal surface is compared with measured data available as typical metrological year, TMY2, and estimated from the different solar models available for Karachi. The models studied are Ahmad models, Raja Models, Hargreaves Model and Black model. The estimation of different empirical correlations and NASA are compared with the measured values. The comparison was done by using statistical techniques like MAPE, MBE, RMSE and t-stats. The results revealed that Hargreaves model is the best model to estimate global radiation for Karachi with minimum value of t-stats while Raja 3 model is the most inappropriate model with highest value of MAPE, RMSE, MBE and t-stat. Overall, the already established empirical correlations can be used for the estimation of solar energy for Karachi for complete utilization of solar energy with minimum errors.
At-Home Pupillometry using Smartphone Facial Identification Cameras

With recent developments in medical and psychiatric research surrounding pupillary response, cheap and accessible pupillometers could enable medical benefits from early neurological disease detection to measurements of cognitive load. In this paper, we introduce a novel smartphone-based pupillometer to allow for future development in clinical research surrounding at-home pupil measurements. Our solution utilizes a NIR front-facing camera for facial recognition paired with the RGB selfie camera to perform tracking of absolute pupil dilation with sub-millimeter accuracy. In comparison to a gold standard pupillometer during a pupillary light reflex test, the smartphone-based system achieves a median MAE of 0.27mm for absolute pupil dilation tracking and a median error of 3.52% for pupil dilation change tracking. Additionally, we remotely deployed the system to older adults as part of a usability study that demonstrates promise for future smartphone deployments to remotely collect data in older, inexperienced adult users operating the system themselves.
Print-A-Sketch: A Handheld Printer for Physical Sketching of Circuits and Sensors on Everyday Surfaces

We present Print-A-Sketch, an open-source handheld printer prototype for sketching circuits and sensors. Print-A-Sketch combines desirable properties from free-hand sketching and functional electronic printing. Manual human control of large strokes is augmented with computer control of fine detail. Shared control of Print-A-Sketch supports sketching interactive interfaces on everyday objects – including many objects with materials or sizes which otherwise are difficult to print on. We present an overview of challenges involved in such a system and show how these can be addressed using context-aware, dynamic printing. Continuous sensing ensures quality prints by adjusting inking-rate to hand movement and material properties. Continuous sensing also enables the print to adapt to previously printed traces to support incremental and iterative sketching. Results show good conductivity on many materials and high spatial precision, supporting on-the-fly creation of functional interfaces.
Faster as Well as Early Measurements from Big Data Predictive Analytics Model

Data processing in large scale system and analyses is the key problems of today's distributed systems in real-time and close to real time. These systems should be able, while meeting these constraints, to process large-scale data inputs, to expand to terabytes or larger. Volume of processing the data over the systems of large scale becomes too large. These systems use parallelization of the data and similar techniques to increase performance. The standards are increasing, though: data analyses are expected almost in real time. Among all the data treatment models Map Reduce is mostly adopted. Due to the processing of data design in this method, once work is finished, the results are returned. The growth of data is not always a preferred batch operating environment: large numbers of applications can benefit from early results. The online aggregation method used in connection databases was first discussed. In this work analysis we discuss techniques that can allow early results to be estimated. We are proposing some changes to the online system for Map Reduce. We show that the properties required for accurate evaluation of the effects of our proposed device design changes. The reduction of data bias algorithm and sampling at the block levels are provided. Consequently, with a series of particular applications and sets of data, the implementation of the proposed design of the system is defined and evaluated.
Knowledge-Based Reasoning Network for Relation Detection

With the rapid growth of large-scale knowledge bases (KBs), knowledge base question answering (KBQA) has attracted increasing attention recently. Relation detection plays an important role in the KBQA system, which finds a compatible answer by analyzing the semantics of questions and querying and reasoning with multiple KB triples. Significant progress has been made by deep neural networks. However, existing methods often concern on detecting single-hop relation without path reasoning, and a few of these methods exploit the multihop relation reasoning, which involves the answer reasoning from the noisy and abundant relational paths in the KB. Meanwhile, the relatedness between question and answer candidates has received little attention and remains unsolved. This article proposes a novel knowledge-based reasoning network (KRN) for relation detection, including both single-hop relation and multihop relation. To address the semantic gap problem in question-answer interaction, we first learn attentive question representations according to the influence of answer aspects. Then, we learn the single-hop relation sequence through different levels of abstraction and adopt the KB entity and structure information to denoise the multihop relation detection task. Finally, we adopt a Siamese network to measure the similarity between question representation and relation representation for both single-hop and multihop relation KBQA tasks. We conduct experiments on two well-known benchmarks, SimpleQuestions and WebQSP, and the results show the superiority of our approach over the state-of-the-art models for both single-hop and multihop relation detection. Our model also achieves a significant improvement over existing methods on KBQA end task. Further analysis demonstrates the robustness and the applicability of the proposed approach.
Evaluation System for the Talent Training Quality of Higher Education Based on the Combination of the Subjective and Objective Evaluation Method and AdaBoost-SVM

This paper mainly researches for the evaluation index system of college students’ cultivation quality. First, we establish a comprehensive evaluation index system of college students’ cultivation quality from three aspects of college students’ knowledge, ability, and quality, to establish the evaluation index system which consists of 3 first-level indexes, 14 second-level indexes, and 40 index observation points. Then, the method of assigning the index weight is optimized and the method of combining subjective and objective weight is adopted. Finally, this paper introduces the algorithm of machine learning as an evaluation method. In order to improve the performance of the algorithm, diversity measure is introduced to ensure that the base learner has good diversity. We proposed a new algorithm AdaBoost-support vector machine (AdaBoost-SVM); it is found that the proposed method gets a high accuracy rate of 91% with a small amount of data in our education datasets. The research results of this paper solve the problems of incomplete index creation, unreasonable index weight assignment, and low efficiency of evaluation methods in the current evaluation of college student’s cultivation quality.
Cavity enhanced parametric homodyne detection of a squeezed quantum comb

A squeezed state with higher-order sidebands is a valuable quantum resource for channel multiplexing quantum communication. However, balanced homodyne detection used in nonclassical light detection has a trade-off performance between the detection bandwidth and clearance, in which the verification of a highly squeezing factor faces a challenge. Here, we construct two optical parametric amplifiers with cavity enhancement; one is for the generation of a -10.5 dB squeezed vacuum state, and the other is for all-optical phase-sensitive parametric homodyne detection. Finally, -6.5 dB squeezing at the carrier with 17 pairs of squeezing sidebands (bandwidth of 156 GHz) is directly and simultaneously observed. In particular, for the cavity-enhanced parametric oscillation and detection processes, we analyze the limiting factors of the detectable bandwidth and measurement deviation from the generated value, which indicates that the length difference and propagation loss between two optical parametric amplifiers should be as small as possible to improve the detection performance. The experimental results confirm our theoretical analysis.
Pholkos – Efficient Large-State Tweakable Block Ciphers from the AES Round Function

This paper proposes Pholkos, a family of heavyweight tweakable block ciphers with state and key sizes of $${\ge }256$$ and tweaks of either 128 or 256 bits. When encrypting large chunks of data under the same key, modes with Pholkos do not require “beyond-birthday security” since it provides “bigger birthday security”. This also makes it a good choice for quantum-secure authenticated encryption modes like QCB. Pholkos runs at 1–2 cycles per byte on Intel 6-th generation and more recent, following design principles from Haraka, AESQ, and the TWEAKEY framework. Building on the AES round function not only boosts software performance but also improves security, employing knowledge from two decades of cryptanalysis of the AES.
Logically Sound Arguments for the Effectiveness of ML Safety Measures

We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening leads to introducing a conservative post-processor after the standard non-max-suppression as a counter-measure. We then propose a semi-formal assurance case for arguing the effectiveness of the post-processor, which is further translated into formal proof obligations for demonstrating the soundness of the arguments. Applying theorem proving not only discovers the need to introduce missing claims and mathematical concepts but also reveals the limitation of Dempster-Shafer’s rules used in semi-formal argumentation.
Communication platform concept for virtual testing of novel applications for railway traffic management systems

In recent years, the railway system has been facing the various challenges of the "digital age". To increase its attractiveness, capacity, sustainability, and security, it needs to improve its' everyday operational and planning process. This can be enabled using new generation digitised and automated Traffic Management Systems (TMS). Nowadays, railway dispatchers need a TMS that offers precise and real-time traffic information as fundamental condition for effective traffic management, and whose performance can be further improved by increasing availability and diversity of sources and data, for which an effective data management platform is required. The EU-funded OPTIMA project is designing and developing a communication platform to manage the connection between several services supporting TMS applications, also enabling their testing. It represents one of the steps required for the development and implementation of a new generation of TMS. This paper describes the concept for the OPTIMA platform which will link TMS applications used by railway dispatchers with infrastructure systems such as signalling and interlocking systems, maintenance, and energy management, as well as with data services (such a weather information) with the aim of providing standardised interfaces and common data structures as basis of a common and standardised communication.
A novel interaction-based methodology towards explainable AI with better understanding of Pneumonia Chest X-ray Images

Abstract In the field of eXplainable AI (XAI), robust “blackbox” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology–Influence score (I-score)—to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. The selected features with high I-score values can be considered as a group of variables with interactive effect, hence the proposed name interaction-based methodology. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems. In investigation of Pneumonia Chest X-ray Image data, the proposed method achieves 99.7% Area-Under-Curve (AUC) using less than 20,000 parameters while its peers such as VGG16 and its upgraded versions require at least millions of parameters to achieve on-par performance. Using I-score selected explainable features allows reduction of over 98% of parameters while delivering same or even better prediction results.
Research on Military Internet of Things Technology Application in the Context of National Security

Intelligent and information-based warfare is developing rapidly, which has a subversive effect on the combat pattern and rules of engagement. In order to firmly grasp the key to the victory of war, we must actively respond to many profound changes such as the mechanism of war victory, the generation mode of combat effectiveness, the mode of battlefield confrontation, and the battle command and decision making. The Military Internet of Things has been applied and gradually extended in all fields of the military, mainly realizing more transparent battlefield, more intelligent battlefield and simpler command process. This paper discusses that the concept of Internet of Things and the military Internet of Things technology are affecting the future information construction of our army, explains that the military Internet of Things technology is widely used to improve the early warning, intelligence sharing ability and support efficiency of the military, and can adapt to the needs of new fields of attack and defense, and puts forward suggestions on the development of military Internet of Things.
VALUE: Understanding Dialect Disparity in NLU

English Natural Language Understanding (NLU) systems have achieved great performances and even outperformed humans on benchmarks like GLUE and SuperGLUE.However, these benchmarks contain only textbook Standard American English (SAE).Other dialects have been largely overlooked in the NLP community.This leads to biased and inequitable NLU systems that serve only a sub-population of speakers.To understand disparities in current models and to facilitate more dialect-competent NLU systems, we introduce the VernAcular Language Understanding Evaluation (VALUE) benchmark, a challenging variant of GLUE that we created with a set of lexical and morphosyntactic transformation rules.In this initial release (V.1), we construct rules for 11 features of African American Vernacular English (AAVE), and we recruit fluent AAVE speakers to validate each feature transformation via linguistic acceptability judgments in a participatory design manner.Experiments show that these new dialectal features can lead to a drop in model performance.
Construction and Knowledge Mining of Traditional Chinese Medicine Ancient Books Bibliographic Abstracts Database Based on Genetic Algorithm and BP Neural Network

With the rapid development of modern science technology and Internet technology, the establishment of a unified and standardized bibliographic summary database to realize the exchange and resource sharing of ancient Chinese medicine bibliography, is the inevitable trend of ancient Chinese medicine bibliography digital service. Firstly, this paper formulates the bibliographic metadata specification of traditional Chinese medicine ancient books (TCMAB), extracts each cataloging file into an XML document in line with the bibliographic metadata specification of TCMAB. Secondly, this paper realizes the unified description of ancient book resources in the database system of TCMAB, uses the native XML database eXist to store and manage the XML documents of all traditional Chinese medicine ancient book resources, and integrates the multimedia data with XML data. Finally, genetic algorithm and BP neural network are used for knowledge mining and discovery, the overall model design of TCMAB bibliographic abstracts database system is proposed based on the construction process of knowledge map. The system platform adopts B/S mode, eXist database management system, PowerSSP streaming media and video server for audio video processing.
Security of IoT-Based e-Healthcare Applications Using Blockchain

The Internet of Things (IoT) has achieved revolutionary growth during the last decade, with tremendous research happening in its application domains. This growth is largely attributable to the ubiquitous nature of technology. Parallel research and development in wearable devices, telemedicine, and e-healthcare have brought IoT devices and application security in these domains to the thrust areas of research. A growing number of healthcare applications are using IoT devices and other wearable smart devices to gather personal medical data and other information of the patients, but inherited security issues related to IoT devices make these applications vulnerable to potential attacks. It has been observed that most of the terminology related to IoT threats, including spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privileges are lacunae for IoT-based healthcare applications. Trust establishment is considered the most important parameter by all practicing medical experts, and the above threats cast aspersions on this basic requirement. The recent developments in blockchain technology have made provisions for potential trust establishment in a fairly large number of use cases. The advantage of blockchain in healthcare applications stems from its tamper-resistant nature, that is, impossibility of alterations in published transactions. This chapter first summarizes the potential attacks on IoT sensor-based healthcare systems along with potential security provisions to such applications by using blockchain technology using a deep literature survey. It then proposes a device-independent integrated approach for establishing a trust-based immutable safety model in IoT-based healthcare applications by securing patient data using blockchain.
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification

Nowadays, real data in person re-identification (ReID) task is facing privacy issues, e.g., the banned dataset DukeMTMC-ReID. Thus it becomes much harder to collect real data for ReID task. Meanwhile, the labor cost of labeling ReID data is still very high and further hinders the development of the ReID research. Therefore, many methods turn to generate synthetic images for ReID algorithms as alternatives instead of real images. However, there is an inevitable domain gap between synthetic and real images. In previous methods, the generation process is based on virtual scenes, and their synthetic training data can not be changed according to different target real scenes automatically. To handle this problem, we propose a novel Target-Aware Generation pipeline to produce synthetic person images, called TAGPerson. Specifically, it involves a parameterized rendering method, where the parameters are controllable and can be adjusted according to the target scenes. In TAGPerson, we extract information from target scenes and use them to control our parameterized rendering process to generate target-aware synthetic images, which would hold a smaller gap to the real images in the specific target domain. In our experiments, our target-aware synthetic images can achieve a much higher performance than the generalized synthetic images on MSMT17, i.e. 47.5% vs. 40.9% for rank-1 accuracy. We will release this toolkit for the ReID community to generate synthetic images at any desired taste. The code is available at: https://github.com/tagperson/tagperson-blender
Hand Gesture Mapping Using MediaPipe Algorithm

Hand gestures are a type of nonverbal communication that may be deployed in a variety of situations, including communication between deaf and deaf-mute individuals, robot control, and human–computer interface home automation, and medical applications, among others. A wide range of approaches have been used in hand gesture-based research publications, as well as those based on designed to operate sensing technology and Internet vision. As a result, the hand sign may be divided into several categories, such as position and gesture, but also dynamic and stable, or a combination of the two. When it comes to enhancing human–computer interaction, gesture recognition has emerged as a critical component to consider. In this paper, we are concentrating on developing the system that simplifies the process of writing code in order to use hand gestures which is now performed manually. Angular and Django were used to create the hand gesture mapper, which is a Web application that provides an graphical user interface via which the user can easily assign functionality to a gesture with a few of mouse clicks. Once the user has completed the setup of his or her gestures, he or she can request the Python code that was created for that configuration. This paper provides the ability to pick any one of the workstation GUI tasks and assign motions to it, as well as the reverse of this functionality. In order to link hand gestures and graphical user interface functionality, the hand gesture mapper reduces the need to write code. It does this by automatically creating code for the user movement configurations that are supplied in the application.