Reviewerly/exHarmony huggingface dataset:
A Hybrid-based Architecture for Web Service Selection

With the rapid growth of Web services in recent years, it is very difficult to choose the suitable web services among those services that offer similar functionality. Choosing the right web service does not only incorporate the issue of finding a Web service base on their functionalities, but also assessing quality aspects of those services. Quality of services (QoS) is considered a distinguishing factor between similar web services and plays a vital role in web service selection. The aim of the model is to evaluate and rank the alternatives consisting of conflicting criteria or criteria with different QoS requirements. To address this issue, this study proposes a model for determining the most suitable candidate web service by integrating AHP (Analytic Hierarchy Process) and VIKOR (Vlsekriterjumska optimizacija I KOmpromisno Resenje) methods. AHP method computes the weights assigned to QoS criteria using pairwise comparison. Thereafter, the ranking of the web services, according to a user preferred criteria, is obtained using VIKOR method. Finally, a JAVA application was implemented using AHP and VIKOR methods. To show and substantiate the proposed model, data from QWS dataset is used.
Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Abstract Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point‐wise learning to rank , where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various prediction problems. In this article, we investigate the effectiveness of MARS and CMARS for point‐wise learning to rank problem. The prediction performance is analyzed in comparison to three well‐known supervised learning methods, artificial neural network (ANN), support vector machine, and random forest for two datasets under a variety of metrics including accuracy, stability, and robustness. The experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.
Target Tracking Based on Siamese Convolution Neural Networks

Target tracking is an important research content in the field of computer vision. There is a problem that speed and precision of tracking can’t be balanced. Aim at this problem, this paper proposes a Siamese-SE deep neural network, which is an improvement in the structure of the Siamese-FC Network that add the SE-Network to the network to improve the feature representation ability by refining and extracting the features. And we use Pearson Correlation Coefficient to calculate similarity of two samples by shifting the matching one by one, which makes the tracking speed and accuracy keep balance. The OTB2015 datasets is used to train and test the network, and TRE, SRE and OPE are used as evaluation criteria in this paper. The experimental results demonstrate that the algorithm proposed is superior to other contrast algorithms including Siamese-FC in tracking effect diagram and three evaluation criteria. This paper verifies the feasibility of the proposed network and meets the requirements of real-time target tracking application.
Knuth Morris Pratt Algorithm in Enrekang-Indonesian Language Translator

String match search as in the case of making a translator matching accuracy is essential. Therefore, there needs to be an implementation of a string matching algorithm that will help get accurate and optimal results. Knuth Morris Pratt is one algorithm to solve problems in the case of string matching. The workings of the KMP algorithm match character by character between pattern and text in the repository. The results of this study indicate that the accuracy of string matching with input in the form of letters and characters by mixing punctuation is 100%. By using 1063 vocabularies, the implementation of the Knuth Morris Pratt algorithm in the Indonesian enrekang language translator has an average processing time of 0.01901 milliseconds and average memory usage of 15,768 MB so that the KMP algorithm can be implemented optimally in the enrekang-Indonesian language translator.
Lower-Limb Strategy Assessment during a Virtual Reality based Dual-Motor-Task

The technical development of the virtual reality platform provides multiple levels to understand human behaviors in simulated environments and to develop interventions for functional rehabilitation. In this study, a dual-task paradigm in a virtual environment is designed where both tasks demand motor skills. Three healthy adults (mean age: 24.3 years) participated in this study. The experiment involved two conditions of overground walking in virtual reality - normal walking and catch and throw a ball while walking. In this work, we investigated the dual-task gait characteristics and the strategy adopted at the lower limb to perform better in the secondary task of throwing the ball. Results show that more balls were thrown during the terminal stance and pre-swing phase of the dominant leg. Thus, the participants utilized the forward momentum built during the foot-to-foot transition by the lagging dominant foot while throwing. This study provides a new and engaging paradigm to analyze dual-motor-task in a virtual reality environment. It can be used as a powerful tool to characterize gait and cognitive performance measures in individuals.
End User and Expert Perceptions of Threats and Potential Countermeasures

Experts often design security and privacy technology with specific use cases and threat models in mind. In practice however, end users are not aware of these threats and potential countermeasures. Furthermore, mis-conceptions about the benefits and limitations of security and privacy technology inhibit large-scale adoption by end users. In this paper, we address this challenge and contribute a qualitative study on end users' and security experts' perceptions of threat models and potential countermeasures. We follow an inductive research approach to explore perceptions and mental models of both security experts and end users. We conducted semi-structured interviews with 8 security experts and 13 end users. Our results suggest that in contrast to security experts, end users neglect acquaintances and friends as attackers in their threat models. Our findings highlight that experts value technical countermeasures whereas end users try to implement trust-based defensive methods.
Transformer Models for Recommending Related Questions in Web Search

People Also Ask (PAA) is an exciting feature in most of the leading search engines which recommends related questions for a given user query, thereby attempting to reduce the gap between user's information need. This helps users in diving deep into the topic of interest, and reduces task completion time. However, showing unrelated or irrelevant questions is highly detrimental to the user experience. While there has been significant work on query reformulation and related searches, there is hardly any published work on recommending related questions for a query. Question suggestion is challenging because the question needs to be interesting, structurally correct, not be a duplicate of other visible information, and must be reasonably related to the original query. In this paper, we present our system which is based on a Transformer-based neural representation, BERT (Bidirectional Encoder Representations from Transformers), for query, question and corresponding search result snippets. Our best model provides an accuracy of ~81%.
Detection of Phishing Attacks using Radial Basis Function Network Trained for Categorical Attributes

Phishing attacks have become the most common cyber-security threat faced by the online users, in which the online credentials of the user are vulnerable to commit the financial crimes. The loss of money and loss of reputation of organization are the notable issues of this attack. Although several anti-phishing strategies have been proposed in the literature, this crucial issue still solicits the detection technique that favors high detection accuracy and low false alarm in the online community. Hence, in this work the Radial Basis Function (RBF) network with its enhanced hyper parameters is proposed for predicting the phishing websites. The proposed detection model utilizes the unsupervised learning for estimating the RBF kernels as well as the spread constant and obtained the categorical RBF for detecting the phishing websites. The proposed approach was tested on benchmark phishing datasets and the performance was compared with the existing neural network classifiers to prove its effectiveness.
Modified Difference Squared Image Based Non Local Means Filter

Image denoising is one of the most important operations in computer vision. Among all the known existing denoising techniques, NLM has proved to perform much better. However, despite its effective performance, the implementation of NLM denoising is limited by a high computational complexity. Lots of Efforts have been put in to reduce this computational complexity and it has been found that summed square images (SSI) and its modified version of difference squared images (DSI) using integral image concept has been much successful in the said attempt. In this paper more contribution has been done to make the NLM faster using the same concept. However, here instead of SSI or DSI, an improved version of DSI has been introduced. The proposed version has been tested on a number of standard test images corrupted with Gaussian noise and the experimental results yielded show that besides increasing the speed, the proposed version also give better visual results than the existing methods. Further, this method has been modified in order to make it effective to remove rician noise in MRI images also. A number of numerical experiments have been conducted to support the above facts.
Frequency Fitness Assignment: Making Optimization Algorithms Invariant Under Bijective Transformations of the Objective Function Value

Under frequency fitness assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and is subject to minimization. FFA renders optimization processes invariant under bijective transformations of the objective function value. On TwoMax, Jump, and Trap functions of dimension s, the classical (1 + 1)-EA with standard mutation at rate 1/s can have expected runtimes exponential in s. In our experiments, a (1 + 1)-FEA, the same algorithm but using FFA, exhibits mean runtimes that seem to scale as s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>2</i></sup> ln s. Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three. On OneMax, LeadingOnes, and Plateau problems, it seems to be slower than the (1 + 1)-EA by a factor linear in s. The (1 + 1)-FEA performs much better than the (1 + 1)-EA on W-Model and MaxSat instances. We further verify the bijection invariance by applying the Md5 checksum computation as transformation to some of the above problems and yield the same behaviors. Finally, we show that FFA can improve the performance of a memetic algorithm for job shop scheduling.
K-Cipher: A Low Latency, Bit Length Parameterizable Cipher

We present the design of a novel low latency, bit length parameterizable cipher, called the "K-Cipher". K-Cipher is particularly useful to applications that need to support ultra low latency encryption at arbitrary ciphertext lengths. We can think of a range of networking, gaming and computing applications that may require encrypting data at unusual block lengths for many different reasons, such as to make space for other unencrypted state values. Furthermore, in modern applications, encryption is typically required to complete inside stringent time frames in order not to affect performance. K-Cipher has been designed to meet these requirements. In the paper we present the K-Cipher design and discuss its rationale. We also present results from our ongoing security analysis which suggest that only 2 to 4 rounds are sufficient to make the cipher operate securely. Finally, we present synthesis results from 2-round 32-bit and 64-bit K-Cipher encrypt datapaths, produced using Intel’s ® 10 nm process technology. Our results show that the encrypt datapaths can complete in no more than 767 psec, or 3 clocks in 3.9-4.9 GHz frequencies, and are associated with a maximum area requirement of 1875 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
A Quantitative Study on the Effects of Learning with Mobile Devices in MOOCs

Massive Open Online Course (MOOC) platforms were initially designed for a desktop learning experience delivered via the Internet. With the increasing acceptance of mobile devices, learners started accessing the MOOC platforms through the browser application on their smartphones and tablets. However, native mobile applications offer better system integration and enhance the learning experience. As the concept of mobile-assisted seamless learning emphasizes the ubiquitous access to learning material, the relevance of mobile devices in the learning process will increase further. This paper investigates the different learning behaviors when using mobile devices on the HPI MOOC Platform. For this, influencing aspects, that cannot always be controlled by the learner, are examined for native applications and mobile websites-such as the size of the screen and the current network state of the mobile device. The results of a quantitative study show highly significant differences between the usage of native applications, mobile websites, and the overall average of the HPI MOOC Platform. It was proven that the size of the screen has a large practical effect when using native applications. Furthermore, course items and videos are more often consumed when the device is connected to a WiFi network This study creates the basis for future research to improve the support of mobile-assisted seamless learning methods for MOOCs.
Implementation of Background Subtraction Method to Detect Movement of Examination Participants

Exam cheating one of the most critical problems of inefficiency and unproductiveness that continuing from primary education to the latest stages of higher education. To overcome the number of test cheating, a system is needed to detect fraud in the exam. In this research, a system was made to detect the exam participants' movement using the background subtraction method. Movements are classified based on what is considered reasonable, and movements that are considered suspicious during the exam. The results of testing the success of 96% were obtained.
Compact and Programmable yet High-Performance SoC Architecture for Cryptographic Pairings

Cryptographic pairings are important primitives for many advanced cryptosystems. Efficient computation of pairings requires the use of several layers of algorithms as well as optimizations in different algorithm and implementation levels. This makes implementing cryptographic pairings a difficult task particularly in hardware. Many existing hardware implementations fix the parameters of the pairing to improve efficiency but this significantly limits the generality and practicality of the solution. In this paper, we present a compact and programmable yet high-performance architecture for programmable system-on-chip platforms designed for efficient computation of different cryptographic pairings. We demonstrate with real hardware that this architecture can compute optimal ate pairings on a Barreto-Naehrig curve with 126-bit security in 2.18ms in a Xilinx Zynq-7020 device and occupies only about 3200 slices, 36 DSPs, and 18 BRAMs. We also show that the architecture can support different types of pairings via microcode updates and can be implemented on other reprogrammable devices with very minor modifications.
Academic library's leadership and stakeholder involvement in research data services

Abstract In the last decade, academic libraries have made progress in establishing themselves as hubs and leaders for research data services on campus. The importance of collaborating with a range of institutional stakeholders, such as the research or information technology office, as well as external partners in developing and delivering research data policy, services, and infrastructure, has been well‐documented. However, there is less evidence as to how libraries play a crucial role in leadership, whether other stakeholders’ involvement actually makes a difference and if so, how they make a difference. As such, the goal of this study is to explore the academic library's leadership role in research data services and how collaboration and partnership with stakeholders and interested parties might impact the maturity of the research data services the library provides. The secondary analysis of the existing survey data found that libraries offer more mature services when they take a primary responsibility in developing the services. It also found internal stakeholders’ and external partners’ involvement leads to more mature services in the selected activities.
Profitability Study of Floating PV and Storage Pumped Hydropower Plant

This article is devoted to the problems of management and profitability of large-scale hybrid photovoltaic and pumped storage power plants. The decision-making methodology for substantiating the taxation is proposed. An optimisation problem is solved. The rationality of changing the rules of payment for transmission grid services is shown. Based on long-term forecasts of photovoltaic generation and changes in electricity market prices, the NPV of the cash flow is estimated and the acceptable time of return on investment is proved. The economic indicators of the technologies under consideration and distributed small photovoltaic plants are compared.
On the use of blind channel response estimation and a residual neural network to detect physical access attacks to speaker verification systems

Spoofing attacks have been acknowledged as a serious threat to automatic speaker verification (ASV) systems. In this paper, we are specifically concerned with replay attack scenarios. As a countermeasure to the problem, we propose a front-end based on the blind estimation of the channel response magnitude and as a back-end a residual neural network. Our hypothesis is that the magnitude response of the channel, obtained by subtracting the log-magnitude spectrum of the observed signal from the prediction of the log-magnitude spectrum average of the observed signal’s clean counterpart, will capture the nuances of room ambiences, recordings and playback devices. The performance of these features is investigated on a benchmark back-end, based on a Gaussian mixture model and on a deep neural network classifier. Our experiments are performed on the 2017 and 2019 Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof) datasets. The benchmark systems are the same as used in the challenges and are based on constant-Q cepstral coefficients (CQCC) and linear-frequency cepstral coefficients (LFCC) features. Experimental results on the 2017 dataset show that the proposed method outperforms the two benchmarks, providing equal-error rates (EER) as low as 7.57% and 11.64%, respectively, for the development and evaluation sets. On the ASVspoof 2019 dataset, in turn, the proposed method outperformed the benchmark using a residual neural network as back-end by yielding tandem detection cost function (t-DCF) and EER as low as 0.1086 and 4.26% on the evaluation set. Lastly, an instrumental (objective) quality assessment is performed on the two datasets and the impact of quality variability on spoofing detection accuracy is discussed.
T-REX

Document-level relation extraction (RE) has recently received a lot of attention. However, existing models for document-level RE have similar structures to the models for sentence-level RE. Thus, they still do not consider some unique characteristics of the new problem setting. For example, in Wikipedia, there is a title for each page and it usually represents the topic entity that is mainly described on the page. In many cases, the topic entity is omitted in the text. Thus, existing RE models often fail to find the relations with the omitted topic entity. To tackle the problem, we propose a Topic-aware Relation EXtraction (T-REX) model. To extract the relations with the (possibly omitted) topic entity, the proposed model first encodes the topic entity by aggregating the information of all its mentions in the document. Then it finds the relations between the topic entity and each mention of other entities. Finally, the output layer combines the mention-wise results and outputs all relations expressed in the document. Our performance study with a large-scale dataset confirms the effectiveness of the T-REX model.
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions

Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.g., text) to the mode of the documents (e.g., images) from a given training set. Such a setup assumes that the training set contains an exhaustive representation of all possible classes of queries. In reality, a retrieval model may need to be deployed on previously unseen classes, which implies a zero-shot IR setup. In this paper, we propose a novel GAN-based model for zero-shot text to image retrieval. When given a textual description as the query, our model can retrieve relevant images in a zero-shot setup. The proposed model is trained using an Expectation-Maximization framework. Experiments on multiple benchmark datasets show that our proposed model comfortably outperforms several state-of-the-art zero-shot text to image retrieval models, as well as zero-shot classification and hashing models suitably used for retrieval.
A Software Factory in the Cloud for Pandemics and other Disasters - Initial Results and Future Directions

Pandemics such as COVID-19 and other disasters in this age require a wide range of cyber-physical solutions such as web portals, mobile apps, IoTs, robots and drones.These solutions need to be customized for different geographical locations with different capabilities and should be produced quickly and at massive scales to meet the specific demands of the disasters.To complicate matters further, several government policies and industry guidelines regulate the deployment and use of such solutions.It is virtually impossible to handcraft the needed solutions individually and manually.A factory model is needed to rapidly build highly customized solutions very much like the auto factories that build millions of highly customized cars to satisfy needed safety requirements.In the same vein, this paper presents initial results from a software factory in the cloud that has produced a Smart Global Village (SGV) with more than 700 smart hubs for 130 countries that span 12 sectors, including disaster management.These highly customized smart hubs, created by using the SPACE Factory in less than 20 minutes per hub, also collaborate with each other that leads to highly creative B2B collaborations at global level.The paper discusses initial results from creating the SGV sandbox by using a factory model and outlines future research directions.