Reviewerly/exHarmony huggingface dataset:
Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.
A Comparison Study of Model Based Collaborative Filtering Using Alternating Least Square and Singular Value Decomposition

Recommender systems are systems that are built to recommend items to users based on a variety of criteria. These systems predict the most likely product that users are likely to buy and are interested in it. The most used recommender system is collaborative filtering. In this research, the author proposes determining the quality of the recommender system by comparing model-based collaborative filtering techniques, i.e., Alternating Least Squares and Singular Value Decomposition with three different characteristics dataset. This study showed Model-Based Collaborative Filtering (Alternating Least Squares and Singular Value Decomposition) able to improve the quality of the recommender system by considering the hyperparameter tuning result. Alternating Least Squares performs slightly better than Singular Value Decomposition in MovieLens and jester dataset.
SampleFix: Learning to Generate Functionally Diverse Fixes

Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning, and NLP have rekindled the hope to eventually fully automate the process of repairing programs. However, previous approaches that aim to predict a single fix are prone to fail due to uncertainty about the true intend of the programmer. Therefore, we propose a generative model that learns a distribution over potential fixes. Our model is formulated as a deep conditional variational autoencoder that can efficiently sample fixes for a given erroneous program. In order to ensure diverse solutions, we propose a novel regularizer that encourages diversity over a semantic embedding space. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to \(45\%\) of the erroneous programs. We additionally show that for the \(65\%\) of the repaired programs, our approach was able to generate multiple programs with diverse functionalities.
Challenges and Security Issues of Online Social Networks (OSN)

In the current scenario, social phishing is the dangerous security issue in Online Social Networks (OSN), in which the attacker tries to steal user’s personal information, account details, passwords, and other login criterion and later which will be misused. It occurs due to the hacker, who misleads the client to open the fake web page and that particular malicious web page masquerading as a worthy link and the user may suppose to enter the personal information and other credentials which will be easily theft by the hacker. Malware installation is also possible in this technique which leads to ransom ware attack or the revealing of sensitive information. This phishing issue is broad, and so many phishing detection techniques are implemented to mitigate specific attacks. This paper surveys the literature on the existing anti-phishing techniques. Various categories and methodologies of anti-phishing techniques are also presented such us phishing detection techniques and recent anti-phishing frameworks
SVD on a Robust Medical Image Watermarking based on SURF and DCT

Communication technology, multimedia, copyright protection, content data have gained attention in recent years. In addition, privacy and confidentiality are also major challenges in handling. A robust hybrid based on speeded-up robust features (SURF), discrete cosine transform (DCT), singular value decomposition or SVD, and chaotic (Arnold's Cat Map) scheme is proposed in this paper. The use of chaotic maps is for watermarking medical images, which can provide protection and security on medical images. In the watermark image, a method is applied that will increase the security of the watermark image, namely Arnold's Cat Maps. SVD method is used to decompose input data into three submatrices. To produce a watermarked image by combining the watermark image and the host image with the SURF-DCT-SVD method, the embedding stage is carried out. At the extraction stage, it will produce a watermark image from the watermarked image. Furthermore, various attacks were carried out against the proposed method. Experimental results show SVD can increase the robustness of DCT and SURF-based watermarking schemes. The proposed watermarking technique is resistant to JPEG compression attacks, noise addition, signal processing, and geometry attacks. In addition, the other state-of-the-art techniques are compared to the performance of the proposed method. Thus, the proposed watermarking scheme can protect ownership and medical records of medical images.
Dynamic range enhancement of the roll angle displacement measurement with birefringence using a polarization camera

A roll angle displacement measurement technique with high resolution for large-range measurement is presented. The proposed technique is based on the birefringence effect and phase detection of polarization interferometry with a polarization camera. The phase difference variation between the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>s</mml:mi> </mml:math> - and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>p</mml:mi> </mml:math> -polarized light induced by a rolling birefringent crystal can be obtained by a polarization camera. The roll angle displacement of the birefringent crystal can be determined from the detected phase difference variation. Several experiments were performed to evaluate the feasibility and performance of the proposed technique. The experimental results demonstrate that the resolution of the system can reach 0.003° with a 10° measurement range. The nonlinear and periodic errors of the system are also analyzed.
Does overlay field of view in head-mounted displays affect spatial memorization?

One of the main targets of criticism of head-mounted displays (HMDs) is the field of view (FOV) size, whether in virtual or augmented reality. This limitation is prominent with optical see-through head-mounted displays (OST-HMD), as those with narrow overlay FOV (OFOV) sizes only provide a small window to view virtual objects. We investigated if restricting this OFOV negatively affects a user’s ability to memorize spatial locations in a simulation of a work environment, and consequently, long-term memory transfer to an equivalent scenario in the real world two days later. To find empirical evidence, we conducted a within-subjects experiment with 18 participants performing in three phases with an OST-HMD, simulated on an immersive HMD. For each phase, they viewed the training scenario with a different OFOV size of the augmentable area (30°, 70°, 110° diagonal). Results from recall tests showed that smaller OFOV size did not significantly affect user’s performance on both short-term and transfer tests, but HMD data revealed that users rotated their heads less with a 110° OFOV. We also found that proximity of objects to memorize had an interaction effect with smaller OFOV sizes. Our findings could have implications on the design and HMD choices of augmented training.
UNET-TTS: Improving Unseen Speaker and Style Transfer in One-Shot Voice Cloning

One-shot voice cloning aims to transform speaker voice and speaking style in speech synthesized from a text-to-speech (TTS) system, where only a shot recording from the target reference speech can be used. Out-of-domain transfer is still a challenging task, and one important aspect that impacts the accuracy and similarity of synthetic speech is the conditional representations carrying speaker or style cues extracted from the limited references. In this paper, we present a novel one-shot voice cloning algorithm called Unet-TTS that has good generalization ability for unseen speakers and styles. Based on a skip-connected U-net structure, the new model can efficiently discover speaker-level and utterance-level spectral feature details from the reference audio, enabling accurate inference of complex acoustic characteristics as well as imitation of speaking styles into the synthetic speech. According to both subjective and objective evaluations of similarity, the new model outperforms both speaker embedding and unsupervised style modeling (GST) approaches on an unseen emotional corpus.
Better Few-Shot Text Classification with Pre-trained Language Model

Recently, pre-trained language models achieve extraordinary performance on numerous benchmarks. By learning the general language knowledge from a large pre-train corpus, the language models could fit for a specific downstream task with a relatively small amount of labeled training data in the fine-tuning stage. More remarkably, the GPT-3 with 175 B parameters performs well in specific tasks by leveraging natural-language prompts and few demonstrations of the task. Inspired by the success of GPT-3, we desire to know whether smaller language models could still have a similarly few-shot learning ability. Unlike the various delicately designed tasks in previous few-shot learning research works, we do it more practically. We present a question-answering-based method to help the language model better understand the text classification task by concatenating a label-related question to each candidate sentence. By leveraging the label-related language knowledge, which the language model has learned during the pre-trained stage, our QA model can outperform the traditional binary and multi-class classification approaches over both English and Chinese datasets. Afterward, we test our QA model by performing few-shot learning experiments on multiple pre-trained language models of different sizes that range from the DistilBERT to the RoBERTa-large. We are surprised to find that even the DistilBERT, which is the smallest language model we tested with only 66 M parameters, still holds undeniable few-shot learning ability. Moreover, the RoBERTa-large with 355 M parameter could achieve a remarkable high accuracy rate of 92.18% with only 100 labeled training data. This result gives people a practical guideline that when a new category of labeled data is needed, only as few as 100 data need to be labeled. Then cooperate with an appropriate pre-training model and classification algorithm, reliable classification results can be obtained. Even without any labeled training data, that is, under the zero-shot learning setup, the RoBERTa-large still achieves a solid accuracy rate of 84.84%. Our code is available at https://github.com/ZhangYunchenY/BetterFs.
Rainmaker: A Tangible Work-Companion for the Personal Office Space

Routines are an important element of day-to-day work life, supporting people in structuring their day around required tasks. Effectively managing these routines is, however, experienced as challenging by many – an issue further amplified by the current work from home lockdown measures. In this paper we present Rainmaker, a tangible device to support people in their working life in the context of their own homes. We evaluate and iterate on our prototype through two qualitative studies, spanning respectively three days (N = 11) and 15 days (N = 2). Our results highlight the perceived advantages of the use of a primarily physical rather than digital tool for work support, allowing users to stay focused on their tasks and reflect on their work achievements. We present lessons for future work in this area and publicly release the software and hardware used in the construction of Rainmaker.
Modeling IoT and Big Data Implementation

As business entering industry 4.0, many emerging technologies have been evolved and utilized by many companies to promote their business performances. Several previous studies have shown lack of knowledge regarding to their implementation. From those studies, there are several factors that should be considered in the implementation including: 1) Business Process Improvement, 2) Marketing Strategies, 3) Business Management Innovation, 4) Business Models, 5) Organizational Culture, 6) Privacy and 7) Ethics. This paper is to propose a model in implementing IoT and Big Data for business performance. A survey is conducted among managers in manufacturing industry using purposive sampling with sample size of 51 managers. After conducting the survey, Smart PLS is used to observe if the developed model is accurate and robust. As expected, the Goodness of Fit (GoF) is 0.74 compared to required 0.67 and the model is said to be robust and accurate. Several findings can be gained including 1) Business Models & Organizational Culture positively influence Business Process Improvement while Privacy and Ethics negatively influence it, 2) Business Process Improvement positively influences Marketing Strategies, Business Management Innovation and Business Performance, and 3) Both Marketing Strategies and Business Management Innovation positively influence Business Performance.
Aggregate Semantics for Propositional Answer Set Programs

Abstract Answer set programming (ASP) emerged in the late 1990s as a paradigm for knowledge representation and reasoning. The attractiveness of ASP builds on an expressive high-level modeling language along with the availability of powerful off-the-shelf solving systems. While the utility of incorporating aggregate expressions in the modeling language has been realized almost simultaneously with the inception of the first ASP solving systems, a general semantics of aggregates and its efficient implementation have been long-standing challenges. Aggregates have been proposed and widely used in database systems, and also in the deductive database language Datalog, which is one of the main precursors of ASP. The use of aggregates was, however, still restricted in Datalog (by either disallowing recursion or only allowing monotone aggregates), while several ways to integrate unrestricted aggregates evolved in the context of ASP. In this survey, we pick up at this point of development by presenting and comparing the main aggregate semantics that have been proposed for propositional ASP programs. We highlight crucial properties such as computational complexity and expressive power, and outline the capabilities and limitations of different approaches by illustrative examples.
Semantic-Fuzzing-Based Empirical Analysis of Voice Assistant Systems of Asian Symbol Languages

Recently, smart voice assistants (VAs) are widely deployed to provide control services via voice commands in IoT systems, e.g., smart home, industrial IoT systems, etc. However, due to the complexity of the application environment and the diversity of voice commands, more and more attacks against VAs cause severe security problems. As voice development platforms allow third-party voice skills to be accessed, adversaries are able to obtain users’ private information by squatting attacks using confusing names. The existing work studied the exploitability of semantic misinterpretation in VA systems on phonetic languages such as English. However, due to the semantic structural difference between phonetic English and symbol-based Asian languages, such as Chinese, the linguistic-model-guided fuzzing tool proposed by the previous work is insufficient to conduct semantic analysis on the VAs of Asian Languages. In this article, we conduct a systematic analysis to evaluate the feasibility of voice misinterpretation attacks to typical Asian language VAs through semantic fuzzing. We develop Harmony-Fuzzer, the semantic fuzzing tool that the fuzzing process is under the guidance of fuzzing rules abstracted from phenomena of speech errors, disfluency, or semantically similar expressions in Chinese corpus. We use Bayesian networks to formulate fuzzing models statistically so that the fuzzing space can be controlled by the probability of fuzzing processing. We use our results to test VAs and design malicious skills to empirically verify the feasibility of squatting attacks. We found that squatting attacks on Chinese VAs are feasible when attackers leverage some linguistic phenomena delicately.
Questionnaire Items for Evaluating Artificial Social Agents - Expert Generated, Content Validated and Reliability Analysed

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 90 researchers worldwide, researching the IVA community interests and practice in evaluating human interaction with an artificial social agent (ASA). The joint efforts have previously generated a unified set of 19 constructs that capture more than 80% of constructs used in empirical studies published in the IVA conference between 2013 to 2018. In this paper, we present expert-content-validated 131 questionnaire items for the constructs and their dimensions, and investigate the level of reliability. We establish this in three phases. Firstly, eight experts generated 431 potential construct items. Secondly, 20 experts rated whether items measure (only) their intended construct, resulting in 207 content-validated items. Next, a reliability analysis was conducted, involving 192 crowd-workers who were asked to rate a human interaction with an ASA, which resulted in 131 items (about 5 items per measurement, with Cronbach's alpha ranged [.60 -- .87]). These are the starting points for the questionnaire instrument of human-ASA interaction.
Performance Analysis of Load Balancing Algorithms in Amazon Cloud

Cloud computing has vast impact in the field of parallel and distributed computing systems today. It enables a massive number of users to access virtualized hardware and software infrastructures with the help of the internet. Consequently, it produces huge traffic into cloud servers. The abrupt growth of this load leads to serious challenges for load balancer of cloud to work out efficiently. The function of load balancer is to distribute the heavy load on servers in cloud environment to obtain maximum utilization of resources by achieving maximum throughput and minimum response time. Various researches have been done to implement better load balancing algorithms to minimize load on servers, but all existing research work in this direction have been developed as an analytical approach in which performance analysis of load balancing algorithms have been performed or compared by creating servers in their systems. The future analysis of this research has been left to implement the load balancing algorithms in real environments. This paper demonstrates the performance analysis of two most popular load balancing algorithms i.e. Round Robin and Least Connections in Amazon Web Service cloud in real-time with the help of performance measuring tool Apache JMeter.
Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of ‘black sheep near neighbours’, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets

In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.
Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition

In the deployment of scene-text spotting systems on mobile platforms, lightweight models with low computation are preferable. In concept, end-to-end (E2E) text spotting is suitable for such purposes because it performs text detection and recognition in a single model. However, current state-of-the-art E2E methods rely on heavy feature extractors, recurrent sequence modellings, and complex shape aligners to pursue accuracy, which means their computations are still heavy. We explore the opposite direction: How far can we go without bells and whistles in E2E text spotting? To this end, we propose a text-spotting method that consists of simple convolutions and a few post-processes, named Context-Free TextSpotter. Experiments using standard benchmarks show that Context-Free TextSpotter achieves real-time text spotting on a GPU with only three million parameters, which is the smallest and fastest among existing deep text spotters, with an acceptable transcription quality degradation compared to heavier ones. Further, we demonstrate that our text spotter can run on a smartphone with affordable latency, which is valuable for building stand-alone OCR applications.