Reviewerly/exHarmony huggingface dataset:
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients

Robustness to adversarial attacks is typically obtained through expensive adversarial training with Projected Gradient Descent. Here we introduce ROPUST, a remarkably simple and efficient method to leverage robust pre-trained models and further increase their robustness, at no cost in natural accuracy. Our technique relies on the use of an Optical Processing Unit (OPU), a photonic co-processor, and a fine-tuning step performed with Direct Feedback Alignment, a synthetic gradient training scheme. We test our method on nine different models against four attacks in RobustBench, consistently improving over state-of-the-art performance. We perform an ablation study on the single components of our defense, showing that robustness arises from parameter obfuscation and the alternative training method. We also introduce phase retrieval attacks, specifically designed to increase the threat level of attackers against our own defense. We show that even with state-of-the-art phase retrieval techniques, ROPUST remains an effective defense.
Hybrid Regularization Algorithm for Efficient Image Deblurring

Image deblurring is a challenging illposed problem with widespread applications. Most existing deblurring methods make use of image priors or priors on the PSF to achieve accurate results. The performance of these methods depends on various factors such as the presence of well-lit conditions in the case of dark image priors and in case of statistical image priors the assumption the image follows a certain distribution might not be fully accurate. This holds for statistical priors used on the blur kernel as well. The aim of this paper is to propose a novel image deblurring method which can be readily extended to various applications such that it effectively deblurs the image irrespective of the various factors affecting its capture. A hybrid regularization method is proposed which uses a TV regularization framework with varying sparsity inducing priors. The edges of the image are accurately recovered due to the TV regularization. The sparsity prior is implemented through a dictionary such that varying weights of sparsity is induced based on the different image regions. This helps in smoothing the unwanted artifacts generated due to blur in the uniform regions of the image.
Exploiting Subjectivity Knowledge Transfer for End-to-End Aspect-Based Sentiment Analysis

While classic aspect-based sentiment analysis typically includes three sub-tasks (aspect extraction, opinion extraction, and aspect-level sentiment classification), recent studies focus on exploring possibilities of knowledge sharing from different tasks, such as document-level sentiment analysis or document-level domain classification that are less demanding on dataset resources. Several recent studies managed to propose different frameworks for solving nearly complete end-to-end aspect-based sentiment analysis in a unified manner. However, none of them studied the possibility of transferring knowledge about their subjectivity or opinion typology between sub-tasks. In this work, we propose subjectivity-aware learning as a novel auxiliary task for aspect-based sentiment analysis. Besides, we also propose another novel task defined as opinion type detection. We performed extensive experiments on the state-of-the-art dataset that show improvement of model performance while employing subjectivity learning. All models report improvement in overall F1 score for aspect-based sentiment analysis. In addition, we also set new benchmark results for the separate task of subjectivity detection and opinion type detection for the restaurant domain of SemEval 2015 dataset.
Corpora compilation for prosody-informed speech processing

Abstract Research on speech technologies necessitates spoken data, which is usually obtained through read recorded speech, and specifically adapted to the research needs. When the aim is to deal with the prosody involved in speech, the available data must reflect natural and conversational speech, which is usually costly and difficult to get. This paper presents a machine learning-oriented toolkit for collecting, handling, and visualization of speech data, using prosodic heuristic. We present two corpora resulting from these methodologies: PANTED corpus, containing 250 h of English speech from TED Talks, and Heroes corpus containing 8 h of parallel English and Spanish movie speech. We demonstrate their use in two deep learning-based applications: punctuation restoration and machine translation. The presented corpora are freely available to the research community.
Logging and Monitoring

Modern applications leverage a variety of cloud services and often span across IaaS, PaaS, and SaaS. In addition, the renewed complexity of applications themselves, the public cloud environments also generate a vast number of signals on their own. Monitoring these environments is different from traditional systems. For example, instead of locking the perimeter down and monitoring activities within your perimeter, you instead abide by the limitations of the cloud provider.
Marginalisation and Precarious Circumstances of People with Albinism (PWA): Information Needs and Services

The study that directed this article investigated the information needs of parents of children with albinism (CWA) in the Khomas region, and determined information services that are appropriate for people with albinism (PWA) in the Khomas region of Namibia, in order to inform the possible design of their information services. Considering PWA as a marginalised user group living under precarious circumstances in Africa, and in the interest of an inclusive information service, a study on information needs was conducted on PWA in the Khomas Region, Namibia. It was conducted within the interpretivism paradigm, following a qualitative research approach, and interviews were conducted with six parents of CWA. In addition, two representatives from organisations that deal with the plight of PWA in Namibia were interviewed as organisational participants (OP). The following information needs were identified in the study: eye-and-skin-related information needs; information on what albinism is; the causes of albinism; information on how to register for the disability grant; and education-related information. The study also revealed that the information needs of parents of CWA differ at each level of the child’s growth. For example, parents stressed needing additional information because as children grow up new needs emerge. OP indicated that they use the following platforms to disseminate information to PWA: radio stations in local languages, community meetings, their websites, and the distribution of flyers in English.
Differentially Private Algorithms for Graphs Under Continual Observation

Differentially private algorithms protect individuals in data analysis scenarios by ensuring that there is only a weak correlation between the existence of the user in the data and the result of the analysis. Dynamic graph algorithms maintain the solution to a problem (e.g., a matching) on an evolving input, i.e., a graph where nodes or edges are inserted or deleted over time. They output the value of the solution after each update operation, i.e., continuously. We study (event-level and user-level) differentially private algorithms for graph problems under continual observation, i.e., differentially private dynamic graph algorithms. We present event-level private algorithms for partially dynamic counting-based problems such as triangle count that improve the additive error by a polynomial factor (in the length T of the update sequence) on the state of the art, resulting in the first algorithms with additive error polylogarithmic in T.We also give e-differentially private and partially dynamic algorithms for minimum spanning tree, minimum cut, densest subgraph, and maximum matching. The additive error of our improved MST algorithm is O(W log^{3/2}T / e), where W is the maximum weight of any edge, which, as we show, is tight up to a (√{log T} / e)-factor. For the other problems, we present a partially-dynamic algorithm with multiplicative error (1+β) for any constant β > 0 and additive error O(W log(nW) log(T) / (e β)). Finally, we show that the additive error for a broad class of dynamic graph algorithms with user-level privacy must be linear in the value of the output solution’s range.
DEVELOPING A FIBO GAME TO LEARN VOCABULARY AT PRIMARY SCHOOL

In the era of globalization, English became one of the most commonly used languages. In addition, English had been Lingua Franca in the world market. Teaching English for the primary level is essential to the creation of a better generation. Nevertheless, not every teacher can develop a joyful learning environment. At the same time, creating a pleasant classroom environment is essential to support optimum learning objectives. As a result, the researcher conducted research on the development of teaching media for young learners. The purpose of this study is to provide an overall representation of the project development of the education media in English subject. For fifth-graders, the FIBO Game can be used to enhance their vocabulary mastery. The researcher utilized Design and Development Research as a research method in the development of the FIBO Game. The study used a qualitative descriptive method to collect the data for the trial.  The respondents of this research were ten fifth-graders from various schools in Tuban. The experts had validated the FIBO Game before the media were tested. The findings of this study revealed that the FIBO Game could encourage students' motivation in learning English. Several students also had problems playing the FIBO Game; for example, they did not grasp the subject properly. In conclusion, the FIBO Game has been shown to meet students' need to learn vocabulary. The implication of these results is deemed for teachers to build a positive learning atmosphere through media use. Keywords: Educational Media, Vocabulary, Young learners
Intelligent recommendation method of literature reading based on user social network analysis

In order to recommend literary works of interest to readers and improve readers' reading efficiency, an intelligent recommendation method for literary reading based on user social network analysis is proposed. The intelligent recommendation model for personalized resource services is optimized for the intelligent recommendation engine. Collect readers' reading interest data sources, based on this, divide the reader's knowledge level, reading behavior, reading style, and reading interest into reading resource recommendation directions, improve the intelligent recommendation engine, personalized service and other modules, so as to comprehensively analyze the attributes of reading resources, Type, efficiency and evolution to realize rapid retrieval, matching, management and recommendation of literary reading. The simulation results show that the recommended error of literature reading of this method is small, and user satisfaction is improved.
Romanian Syllabification Using Deep Neural Networks

Syllabification may be considered trivial for humans, but it can prove to be a challenging task in terms of automated text analysis. In this study, we explore three approaches to syllabify words in Romanian using state-of-the-art deep learning architectures in sequence prediction, namely BiLSTM, CNN, and transformer. In contrast to previous approaches, our models take into account the part of speech of the word, which in return can weigh heavily in situations where words have the same written form, but different syllabification. Our best model obtains an accuracy of approximately 98% using a conditional random field on top of the BiLSTM architecture, surpassing all previous state-of-the-art models. Our model represents a building block for multiple smart learning ecosystems, ranging from better hyphenation software for text evaluation, to text-to-speech and speech-to-text frameworks employed in intelligent houses or personal assistants.
Predicting cognitive performance using eye-movements, reaction time and difficulty level.

Cognitively challenging tasks require complex coordination of information beyond visual input. Predicting accuracy on such tasks has potential applications in education and industry. Task difficulty is associated with increases in reaction time and variation in eye tracking indices. Critically, machine learning has not yet been used to predict accuracy on cognitive tasks with multiple difficulty levels. We report data on 57 (34 females; 20-30 years) participants who completed visuospatial tasks of mental attentional capacity with six levels of difficulty while their eye movements were recorded using EyeLink Portable Duo SR Research eye-tracker with 1ms temporal resolution (at 1000 Hz frequency) in remote head-free-to-move mode. Results show that task accuracy scores can be robustly predicted when all variables (e.g., eye-tracking, difficulty level and reaction time) are considered together (R2 = .80). Reaction time, difficulty level and eye tracking metrics are also effective independent predictors with R2 equaling .73, .58, and .36, respectively. Analyses for feature importance suggest eye-tracking indices with the most importance for the models include the number of fixations, number of saccades, duration of the current fixation and pupil size. Notably, our machine learning algorithms target a prediction question, rather than a classification one, and the current algorithm can be useful for future research and applications in other contexts where visuospatial processing is required. Theoretically, findings show common and distinct metrics that can inform theories of cognition and vision science.
Professional Education: Goals, Objectives, Processes

Discussed is the problem of achieving the required quality of professional education from the point of view of implementing a systematic approach: goals, tasks, processes. Shown the importance of the correct formulation of goals and objectives in the curricula, which have become available on the websites of universities. Their formulation helps the teacher to correctly build the learning process. Examples of structuring the goals of studying educational material are given. Shown the advantages of the integrated educational process carried out by the project team — the scientific and methodological council for the specialty. Attention is paid to the need for teachers to demonstrate their leadership qualities.
Blockchain based Tamper Proof Certificates

Document is the main proof for showing identity of a person and nowadays there has been many cases related to document tampering. Most of the documents like marksheet, birth certificate, identity card, etc. can be easily tampered and produced, sometimes the tampered document looks much more real and it becomes difficult for the organization or the verifier to verify the document. This research paper will try to overcome the issues with the help of blockchain technology. Blockchain is a decentralized peer-to-peer mechanism that provides protection, trustworthiness, legitimacy, immutability, and transparency. In this proposed system hashing technique is used for encryption. By using this technology people can request for the necessary documents and verifier can easily verify the documents using hash value.
Emotion recognition of perceived genuine or posed expressions using web-based eye tracking

In everyday life emotions may be genuine (e.g. smiling at a loved one) or posed (e.g. smiling to be polite). The current study investigates how perceived sincerity influences emotion recognition. A collection of naturalistic expressions was used, taken from real-world media and normed for perceived genuineness. Gazer, a web-based eye tracking software, was used to record gaze during the trials.
Phone duration modeling for speaker age estimation in children

Automatic inference of paralinguistic information from speech, such as age, is an important area of research with many technological applications. Speaker age estimation can help with age-appropriate curation of information content and personalized interactive experiences. However, automatic speaker age estimation in children is challenging due to the paucity of speech data representing the developmental spectrum, and the large signal variability including within a given age group. Most prior approaches in child speaker age estimation adopt methods directly drawn from research on adult speech. In this paper, we propose a novel technique that exploits temporal variability present in children's speech for estimation of children's age. We focus on phone durations as biomarker of children's age. Phone duration distributions are derived by forced-aligning children's speech with transcripts. Regression models are trained to predict speaker age among children studying in kindergarten up to grade 10. Experiments on two children's speech datasets are used to demonstrate the robustness and portability of proposed features over multiple domains of varying signal conditions. Phonemes contributing most to estimation of children speaker age are analyzed and presented. Experimental results suggest phone durations contain important development-related information of children. The proposed features are also suited for application under low data scenarios.
Automatic Text Summarization Using Deep Learning

Text summarization is a process of extracting collecting important information from original text and present the information in the form of summary. Text summarization has become the necessity of many applications for example search engine, business analysis, market review. Summarization helps to gain required information in less time. This paper is an attempt to summarize and present the view of text summarization from every aspect from its beginning till date. The two major approaches i.e., extractive and abstractive summarization is discussed in detail. The technique deployed for summarization ranges from structured to linguistic. In Indian many languages also the work has being done, but presently they are in infancy state. This paper provides an abstract view of the present scenario of research work for text summarization. In an computerized machine of summarization which is wreck up into the subsequent steps: Pre-processing (sentence segmentation, tokenization, give up words removal), Feature Extraction, Sentence Scoring, Sentence Ranking and Summary Extraction. Finally, this paper collects the most crucial today's and applicable lookup interior the zone of the textual content summarization to assessment and assessment for future research.
Suboptimally Solving the Watchman Route Problem on a Grid with Heuristic Search

In the Watchman Route Problem (WRP) we are given a grid map with obstacles and the task is to (offline) find a (shortest) path through the grid such that all cells in the map can be visually seen by at least one cell on the path. WRP was recently formalized and optimally solved with heuristic search. In this paper we show how the previous optimal methods can be relaxed and modified to obtain suboptimal solvers that are much faster than the optimal solvers without sacrificing too much the quality of the solution. In particular, we present three methods that intelligently prune away large subtrees. We then derive bounded suboptimal solvers, suboptimal solvers without bounds and anytime variants of these. All these algorithms are backed up with experimental evidence that show their benefits compared to existing approaches.
Entrepreneurship Development in Nigeria: Prospects and Challenges

The paper took a look at the challenges of entrepreneurship development in Nigeria with specific interest on Borno State. It also tries to consider the need for developing entrepreneurship skills in transforming challenges into business opportunities in Nigeria. To achieve this, entrepreneurs were selected using the stratified sampling technique from the three senatorial zones of the state. Analysis of Variance (ANOVA) was used to validate the results at 5% level of significance. The challenge of unemployment, insecurity and poverty facing the citizens of the state stands out as the major challenges of entrepreneurship development in Borno State, and the findings of the study. While the government braced up to these challenges through various programmes and entrepreneurial initiatives, a key solution is the equipping of the graduates and professionals with entrepreneurial skills that can make them employable and become employers of labour to enhance their self-sustainability. The findings revealed that there is a significant relationship between the entrepreneurship challenges in the three senatorial districts since the multiple comparisons shows a level of significance of .000 in all the 3 senatorial districts, compared to the 5% level of significance.it is recommended that the problems of insecurity and unemployment should be well tackled by government across the senatorial zones in the state.
Network Security System Analysis Using Access Control List (ACL)

Communication using internet media is a mandatory requirement today to carry out various activities. Communication through the internet will pass through public access, where many people use public access. When communication is in progress, many things can happen before the message reaches its destination. One of them is the occurrence of hacking attempts from irresponsible parties. Therefore, it is necessary to secure the communication process, and many network security methods can be used to secure the communication process in the internet network. Access Control List or ACL is one method that can be used to secure communication in the network. ACL makes it easy to block specific devices and ports to prevent network communication. The results of the ACL implementation have the impact of avoiding access so that not all devices or protocols can access certain communications, thereby increasing network security in the process.