Reviewerly/exHarmony huggingface dataset:
P3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning

Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training. To mitigate these gaps, we propose Pre-trained, Prompt-learned and Pre-finetuned Neural Ranker (P^3 Ranker). P^3 Ranker leverages prompt-based learning to convert the ranking task into a pre-training like schema and uses pre-finetuning to initialize the model on intermediate supervised tasks. Experiments on MS MARCO and Robust04 show the superior performances of P^3 Ranker in few-shot ranking. Analyses reveal that P^3 Ranker is able to better accustom to the ranking task through prompt-based learning and retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning, resulting in data-efficient PLM adaptation. Our code is available at https://github.com/NEUIR/P3Ranker.
Librarian’s perception on data librarianship core concepts: a survey of motivational factors, challenges, skills and appropriate trainings platforms

Purpose Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services. Design/methodology/approach A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data. Findings LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills. Research limitations/implications The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others. Originality/value This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.
Development of an Electronic Educational Kit with Android Application That Test Student Knowledge in Series and Parallel Resistor for Electrical Circuit (Res-Circuit Quiz Board)

One of the important subject inElectrical Engineering course is Electrical Circuit. The subject is known to be challenging as this is the first time student being expose to the Electrical Engineering subject. In order to address this, this paper is proposing an educational board, named Res-Circuit Quiz Board. The educational board is designed to test student’s knowledge in series and parallel resistor connection for subject Electrical Circuit. An android based application is designedas interface for the quiz board to connect to through Bluetooth. The educational quiz board will give feedback to student based on the circuit connection done by the student. The effectiveness of the educational quiz board is measured using a survey done to the target audience. The result shows positive feedback from the respondents.
Impact of using multi-levels of parallelism on HPC applications performance hosted on Azure cloud computing

The use of high-performance computing (HPC) applications has increased progressively in scientific research and industry. Cloud computing attracts HPC users because of its extreme cost efficiency. The reduced cost is the result of the successful employment of multilayer-virtualisation enabling dynamic elastic resource-sharing between different tenants. In this paper, we evaluate the impact of using multi-levels of parallelism on computationally intensive parallel tasks hosted on a cloud virtualised HPC cluster. We use multi-levels of parallelism through a set of experiments employing both message passing and multi-threading techniques. Our evaluation addresses two main perspectives, the performance of applications and cost of running HPC applications on clouds. We use millions of operations per seconds (MOPS) and speed-up to evaluate the computational performance. To evaluate the cost we use United States Dollar/MOPS (USD/MOPS). The experiments on two different clouds are compared against each other and with published results for Amazon EC2 cloud. Results show that balancing the workload between processes and threads per process is the key factor to maintain high performance with reasonable cost.
Statistical assessment of nonlinear manifold detection-based software defect prediction techniques

Prediction of software defects has immense importance for obtaining desired outcome at minimised cost and so attracted researchers working on this topic applying various techniques, which were not found fully effective. Software datasets comprise of redundant features that hinder effective application of techniques resulting inappropriate defect prediction. Hence, it requires newer application of nonlinear manifold detection techniques (nonlinear MDTs) that has been examined for accurate prediction of defects at lesser time and cost using different classification techniques. In this work, we analysed and tested the effect of nonlinear MDTs to find out accurate and best classification technique for all datasets. Comparison has been made between the results of without or with nonlinear MDTs and paired two-tailed T-test has been performed for statistical testing and verifying the performance of classifiers using nonlinear MDTs on all datasets. Outcome revealed that among all nonlinear MDTs, FastMVU makes most accurate prediction of software defects.
Enlightening Patients with Augmented Reality

Enlightening Patients with Augmented Reality (EPAR) enhances patient education with new possibilities offered by Augmented Reality. Medical procedures are becoming increasingly complex and printed information sheets are often hard to understand for patients. EPAR developed an augmented reality prototype that helps patients with strabismus to better understand the processes of examinations and eye surgeries. By means of interactive storytelling, three identified target groups based on user personas were able to adjust the level of information transfer based on their interests. We performed a 2-phase evaluation with a total of 24 test subjects, resulting in a final system usability score of 80.0. For interaction prompts concerning virtual 3D content, visual highlights were considered to be sufficient. Overall, participants thought that an AR system as a complementary tool could lead to a better understanding of medical procedures.
A New semi-supervised clustering for incomplete data

Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods.
Provisioning big data applications as services on containerised cloud: a microservices-based approach

We present the fundamental challenges for dynamic provisioning of big data applications. The findings are based on our previous experience in this domain, as well as a comprehensive study on a selected set of state-of-the-art tools in the big data ecosystem. We then incorporate these findings in a framework aiming at dynamically provisioning big data applications as services on containerised cloud. The innovations behind the framework are to optimise the whole lifecycle of big data applications in a holistic manner by the adoption of microservices(μServices) methodologies. The feasibility of our approach is verified through a case study of provisioning a large-scale user traffic data processing application in a private cloud environment backed by Kubernetes. We also show that while hosting big data applications in containerised cloud can significantly eliminate the presumed complexity of deployment and operation, it in the meantime also comes with a certain amount of cost in terms of learning curve and traceability. Our research helps technical decision makers to assess the adoption of microservices for big data applications more objectively.
Attention-based word-level contextual feature extraction and cross-modality fusion for sentiment analysis and emotion classification

Multimodal affective computing has become a popular research area, due to the availability of a large amount of multimodal content. Feature alignment between the modalities and multimodal fusion are the most important issues in multimodal affective computing. To address these issues, the proposed model extracts the features at word-level and forced alignment is used to understand the time-dependent interaction among the modalities. The contextual information among the words of an utterance and between the nearby utterances is extracted using bidirectional long short term memory (LSTM). Weighted pooling based attention model is used to select the important features within the modalities and importance of each modality. Information from multiple modalities is fused using a cross-modality fusion technique. The performance of the proposed model was tested on two standard datasets such as IEMOCAP and CMU-MOSI. By incorporating the word-level features, feature alignment, and cross-modality fusion, the proposed architecture outperforms the baselines in terms of classification accuracy.
UT Austin Villa: RoboCup 2018 3D Simulation League Champions

The UT Austin Villa team, from the University of Texas at Austin, won the 2018 RoboCup 3D Simulation League, winning all 23 games that the team played. During the course of the competition the team scored 143 goals without conceding any. Additionally, the team won the RoboCup 3D Simulation League goalie challenge. This paper describes the changes and improvements made to the team between 2017 and 2018 that allowed it to win both the main competition and goalie challenge.
Fake Profile Identification in Online Social Networks

There is a tremendous increase in technologies these days.. Mobiles are becoming smart. Technology is associated with online social networks which has become a part in every one’s life in making new friends and keeping friends , their interests are known easier. But this increase in networking online make many problems like faking their profiles, online impersonation having become more and more in present days. Users are fed with more unnecessary knowledge during surfing which are posted by fake users. Researches have observed that 20% to 40% profiles in online social networks like facebook are fake profiles. Thus this detection of fake profiles in online social networks results into solution using frameworks.
A survey on data stream, big data and real-time

Real-time concept is being widely used by a society that seeks to speed communications, decisions and their daily activities. Even though this term is not used with the necessary conceptual precision, it makes clear the importance that time exerts on computer systems. Nowadays, the big data scenario, this concept is important and used with different meanings, which can define failure or successful of applications. This article aims to present a systematic literature review on the topics of data stream, big data and real-time. For this, we developed a protocol revision in which were determined research questions, the search term, the search source and the inclusion and exclusion criteria of articles. After an extensive study, we classify the articles selected in seven categories according to real-time concept used. Finally, we present a discussion that shows that there is not convergence on real-time concepts in the big data literature.
Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss

This paper aims to present a methodology for sepsis prediction from clinical time-series data.Sepsis is one of the most threatening states which could occur while treating a patient at the intensive care unit.Therefore its prediction could significantly improve the quality of the patient treatment.In this work, we address the problem of sepsis prediction with Long Short-Term Memory (LSTM) network with specialized deep architecture with residual connections.The output of the network is sepsis prediction score at each point in time.Feature normalization into the fixed range of values is applied including replacing missing values with numerical representation from outside the normalized range.Therefore, the LSTM network is able to include missing values in the learning process.Also, the rarity of sepsis occurrence in the provided dataset is a challenging problem.This problem is addressed by the application of dice loss providing automatically weighted classes by the occurrence of the feature.The proposed method leads to 0.281 normalized
Ensemble Machine Learning Model for Software Defect Prediction

Software defect prediction is a significant activity in every software firm. It helps in producing quality software by reliable defect prediction, defect elimination, and prediction of modules that are susceptible to defect. Several researchers have proposed different software prediction approaches in the past. However, these conventional software defect predictions are prone to low classification accuracy, time-consuming, and tasking. This paper aims to develop a novel multi-model ensemble machine-learning for software defect prediction. The ensemble technique can reduce inconsistency among training and test datasets and eliminate bias in the training and testing phase of the model, thereby overcoming the downsides that have characterized the existing techniques used for the prediction of a software defect. To address these shortcomings, this paper proposes a new ensemble machine-learning model for software defect prediction using k Nearest Neighbour (kNN), Generalized Linear Model with Elastic Net Regularization (GLMNet), and Linear Discriminant Analysis (LDA) with Random Forest as base learner. Experiments were conducted using the proposed model on CM1, JM1, KC3, and PC3 datasets from the NASA PROMISE repository using the RStudio simulation tool. The ensemble technique achieved 87.69% for CM1 dataset, 81.11% for JM1 dataset, 90.70% for PC3 dataset, and 94.74% for KC3 dataset. The performance of the proposed system was compared with that of other existing techniques in literature in terms of AUC. The ensemble technique achieved 87%, which is better than the other seven state-of-the-art techniques under consideration. On average, the proposed model achieved an overall prediction accuracy of 88.56% for all datasets used for experiments. The results demonstrated that the ensemble model succeeded in effectively predicting the defects in PROMISE datasets that are notorious for their noisy features and high dimensions. This shows that ensemble machine learning is promising and the future of software defect prediction.
A Digital Cascoded Signature Attenuation Countermeasure with Intelligent Malicious Voltage Drop Attack Detector for EM/Power SCA Resilient Parallel AES-256

Computationally secure cryptographic algorithm leaks meaningful side-channel information which can be exploited to extract confidential data. Circuit level countermeasures against power/ EM side channel attack (SCA) like current equalizer [1], series LDO with randomization [2], integrated buck regulator (IBR) [3] had been demonstrated recently providing moderate security (~10M) against Correlational Power/EM attack (CPA/CEMA). Current domain signature attenuation (CDSA) [4] achieved >1B minimum-traces-to-disclosure (MTD) with a single analog technique. Randomized non-linear LDO cascaded with arithmetic countermeasure achieves similar security [5], albeit with combination of two techniques. A process-scalable version of [4] achieved ~250M MTD [6] with bleed-RO randomization, and ~20M MTD without it [7]. Cascading this solution with TVTF [6] provided highest security till date. On the other hand, digital friendly NL-DLDO suffers from higher overhead. Arithmetic countermeasure is fully synthesizable, but algorithm specific, cannot be easily ported to another encryption algorithm. In [6], the digital friendly current source (CS) brings the benefit of signature attenuation in digital domain, however, lacks the high attenuation and MTD from Analog Cascode CS in [4]. Most importantly, a dedicated attack on the state-of-the-art (SoA) countermeasures is still left unexplored. This work for the first time explores the possibility of an attack on signature attenuation hardwares using malicious reduction of voltage and utilizes an intelligent attack detector circuit to detect such attacks and adapt to it to guarantee the efficacy of such signature attenuation-based countermeasures. Moreover, an improved digital-friendly cascoded CS is implemented achieving the highest signature attenuation with digital-friendly technique till date, i.e. a ~10x improvement without RO-bleed randomization. A detailed progress of countermeasure along with motivation is presented in Fig. 1. The 65nm CMOS test chip (side figure) consists of a parallel AES-256 encryption engine along with an Intelligent Digital Cascoded Signature Attenuation Circuit (i-DCSAC) as countermeasure and malicious attack detector.
Fraud Detection and Analysis for Insurance Claim using Machine Learning

Insurance Company working as commercial enterprise from last few years have been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implement machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library.
Fault Tolerance Byzantine Algorithm for Lower Overhead Blockchain

A new algorithm for practical Byzantine fault tolerance (PBFT), called score-PBFT or S-PBFT, is proposed to solve the problems of high communication overhead and low algorithm efficiency. This algorithm is based on the characteristics of the consortium chain. The scoring mechanism for nodes is added. All the nodes are broken up into consensus nodes, candidate nodes, and early nodes. To make sure the consensus nodes are as reliable as possible, the nodes are changed dynamically based on how each node is behaving. Improved: the election method for the controller node has been changed. The node’s score and behaviour are used as the election basis to make the algorithm more stable. In this paper, we want to improve the consensus protocol’s execution process, cut down on how many nodes are involved in the consensus process, simplify it, and make it more efficient. Results show that, when compared with the PBFT algorithm, the S-PBFT algorithm has a shorter consensus delay, less communication overhead and throughput, and better consensus node reliability.
Maximum Structural Generation Discrepancy for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) has recently become an appealing research topic in visual recognition, since it exploits all accessible well-labeled source data to train a model with high generalization on target domain without any annotations. However, due to the significant domain discrepancy, the bottleneck for UDA is to learn effective domain-invariant feature representations. To fight off such an obstacle, we propose a novel cross-domain learning framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Maximum Structural Generation Discrepancy</i> (MSGD) to accurately estimate and mitigate domain shift via introducing an intermediate domain. First, the cross-domain topological structure is explored to propagate target samples to generate a novel intermediate domain paired with the specific source instances. The intermediate domain plays as the bridge to gradually reduce distribution divergence across source and target domains. Concretely, the similar category semantic across source and intermediate features tends to naturally conduct the class-level alignment on eliminating their domain shift. In terms of no target annotation, the domain-level alignment manner is suitable to narrow down the distance between intermediate and target domains. Moreover, to produce high-quality generative instances, we develop the class-driven collaborative translation (CDCT) module to generate class-consistent cross-domain samples in each mini-batch with the assistance of pseudo-labels. Extensive experimental analyses on five domain adaptation benchmarks demonstrate the effectiveness of our MSGD on solving UDA problem.