Reviewerly/exHarmony huggingface dataset:
A Bankable Pictorial Password Authentication Approach

Secrecy has always been a tremendous issue, for data creation and exchange. The biggest question is how to give access to only authorized users. For authorization user needs Authentication which can only be checked by their identity proof. The Password entry system is widely used for identification purposes. Password assures that a device or information can only be used by those who have been given rights to access them. Large systems are facing security issues because of text- password that are most commonly used. An alternative approach for authentication is Graphical passwords; these passwords are used to suppress known problems with traditional authentication methods. In this paper Knowledge-Based authentication techniques have been studied and the need for the graphical password has been discussed. This paper analyzes a Pure Recall graphical password technique known as Draw A Secret and proposes the Advance-DAS technique which overcomes all security and usability problems faced in DAS. The proposed technique presents promising results in terms of security and usability and memorability.
Course-Aided Measurement-to-Track Association for Target Tracking With Compact HF Radar

Data association is an important procedure in target tracking with high-frequency surface wave radar (HFSWR). In most existing target tracking methods, only range, azimuth, and Doppler velocity parameters are involved in data association. However, for data association under scenarios of inaccurate azimuth measurements, dense clutter, and multiple targets, employing these three kinematic parameters alone may not achieve satisfactory performance. In this paper, from the perspective of track consistency, the vessel course is investigated and explored as a stable feature for data association, and a course-aided measurement-to-track association method is developed. The target course data sequence is firstly estimated by applying a course estimation method to an initiated short track, then each candidate plot within a validation gate is added to the track to generate one candidate course, the candidate plot that causes the minimum course difference is assigned to update the target track. Experimental results with field data demonstrate that the data association accuracy is improved by the proposed method, and better track continuity is achieved.
Internal Dictionary Matching.

We introduce data structures answering queries concerning the occurrences of patterns from a given dictionary $$\mathsf {D}$$ in fragments of a given string T of length n. The dictionary is internal in the sense that each pattern in $$\mathsf {D}$$ is given as a fragment of T. This way, $$\mathsf {D}$$ takes space proportional to the number of patterns $$d=|\mathsf {D}|$$ rather than their total length, which could be $$\varTheta (n\cdot d)$$ . In particular, we consider the following types of queries: reporting and counting all occurrences of patterns from $$\mathsf {D}$$ in a fragment $$T[i \mathinner {.\,.}j]$$ and reporting distinct patterns from $$\mathsf {D}$$ that occur in $$T[i \mathinner {.\,.}j]$$ . We show how to construct, in $$O((n+d) \log ^{O(1)} n)$$ time, a data structure that answers each of these queries in time $$O(\log ^{O(1)} n+| output |)$$ . The case of counting patterns is much more involved and needs a combination of a locally consistent parsing with orthogonal range searching. Reporting distinct patterns, on the other hand, uses the structure of maximal repetitions in strings. Finally, we provide tight—up to subpolynomial factors—upper and lower bounds for the case of a dynamic dictionary.
A Gaze-Supported Mouse Interaction Design Concept for State-of-the-Art Control Rooms

Control rooms represent critical infrastructures important for security and well-being of humans. While they have changed considerably with respect to information technologies within the last 30 years, user interfaces are, as our studies revealed, still characterized by windows, icons, menus, and pointers (WIMP). Further research on novel interaction design concepts for multi-monitor WIMP applications is necessary in addition to smart control room and pervasive computing environments approaches. In this study, we examined how control room operators can be supported in their daily work with multi- monitor applications. Based on human-centred design activities, the gaze-supported mouse interaction design concept “Look & Drop”, approaching the most common interaction problems, e.g. moving the mouse pointer to distant screens, was developed. Results of a laboratory study, expert reviews and a proof of concept installation at a previously unknown control room indicate that Look & Drop could support operators at state-of-the-art control room workstations.
Analisis perilaku siswa yang kecanduan game online mobile legend

Research objectives 1) determine the symptoms of students addicted to the online game Mobile Legend. 2) find out the factors that influence students addicted to the Mobile Legend online game. 3) find out the impact of Mobile Legend online game addiction on students. 4) the role of self-management techniques to reduce addiction to the Mobile Legend online game. This research is qualitative with a type of case study research. The research subjects are 2 students who are addicted to the legendary online mobile game. The research instruments used were interviews, observation and documentation. Research results: 1) Symptoms of students who are addicted to online games: a) cognitive salience b) behavioral salience c) euphoria d) external conflict e) internal conflict f) tolerance g) withdrawal symptoms. h) relapse and reinstatement 2) Factors affecting students addicted to online games are: a) reward, feedback on games b) reduce boredom towards real life c) eliminate the loneliness streotype d) social inability for players who are addicted e) low self esteem and self efficacy f) virtual environments. 3) The impact on students' learning achievements addicted to online games namely; lazy to do the task, low report card grades, feeling weak and lacking enthusiasm when I wake up in the morning, and his emotions are not stable, enthusiasm for learning is low. 4) Tutor teacher's efforts to overcome online game addiction on two students namely: a) Using an individual counseling approach, b) Conducting home visits.
Detecting Conversational Toxicity Using Neural Networks

Along with providing a platform for users to express their views freely and connect to the masses, it also becomes a place for hateful behavior and cyber bullying. This negativity sometimes becomes the cause to stop people from expressing their point of view. Issues like this happen quite often, and the modulators have limited capabilities to deal with it manually. Even though much research is going on to determine whether a post or comment is toxic or not, social media platforms are still looking for efficient and faster ways to detect and remove toxic content. Automating this will not only help in identifying toxic material, but it will also eventually promote discussions online at the same time maintaining user safety. The paper addresses this issue with the aid of Wikipediaâ€™s talk page edits dataset, found on Kaggle, which is used to train the deep learning model, which ultimately classifies the data into six categories: toxic, severe toxic, obscene, threat, insult, and identity hate. The deep learning technique used here is Long Short-Term Memory (LSTM) with the GloVe word embeddings.
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules

Neural models for morphological inflection have recently attained very high results.However, their interpretation remains challenging.Towards this goal, we propose a simple linguistically-motivated variant to the encoderdecoder model with attention.In our model, character-level cross-attention mechanism is complemented with a self-attention module over substrings of the input.We design a novel approach for pattern extraction from attention weights to interpret what the model learn.We apply our methodology to analyze the model's decisions on three typologicallydifferent languages and find that a) our pattern extraction method applied to cross-attention weights uncovers variation in form of inflection morphemes, b) pattern extraction from self-attention shows triggers for such variation, c) both types of patterns are closely aligned with grammar inflection classes and class assignment criteria, for all three languages.Additionally, we find that the proposed encoder attention component leads to consistent performance improvements over a strong baseline. 4From this perspective, we formulate the following desired properties for retrieving linguistic structures of inflection.For a given grammatical category (e.g.MSD V;IND;PRS;3;PL from the previous example in Italian), Query No. of/Acc Patterns gold target=*scono & MSD=msd it 23/1.00*re: 9/1.0 (in| z| o| ti| chi| re) *ire: 7/1.0 (s| col| or| ire) *ir: 6/1.0 (re| in| ser| ir| si) *cir*: 1/1.0 (in| fer| o| cir |si) gold target=*ano & MSD=msd it 189/1.00*are: 149/1.0(z| am| pic| are) *arsi: 26//1.0(im| pa| per| arsi) *car*:3/1.0(ri|mb|ec|car|si) *izzarsi:2/1.0(dest|abil|izz|arsi) *iarsi:2/1.0 (di|lan|i|arsi) *par*:2/1.0(dis|col|par|si) *ciarsi:1/1.0(au|to|den|un|ci|arsi) *mar*:1/1.0(in|for|mar|si) *rarsi:1/1.0 (gi|ost|r|arsi) *itarsi:1/1.0(ri|abil|it|arsi) *quar*:1/1.0 (sci|ac|quar|si) gold target=*ono & !(*scono) & MSD=msd it 41/0.95*ere: 18/0.95(ri| otten| ere) *dere: 10/1.0 (te| le| ve| dere) *ger*: 3/1.0 (cos| par| ger| si) *re:3/1.0(servi|re) *e:1/0.0(ri|ro|m|per|e) *ir*:1/1.0(1908:s|ent|ir|si) *si:1/1.0(es|p|or|si) *ire:1/1.0(ri|di|ven|ire) *er*:1/1.0(r|aggi|ung|er|si) *mer*:1/1.0(ass|u|mer|si)Table 6: Italian Self-Att sub Patterns.MSD query msd it is V;IND;PRS;3;PL.Number of examples (No of ) and accuracy (Acc) are shown per selection with query and per group pattern.For each query, we list all extracted lemma patterns (sorted by frequency in a decreasing order) along with one segmented lemma example (in parentheses) mapped to the pattern.
Zero-knowledge Proof (ZKP) and Privacy Preserving

Cryptography has proven to be one of the most contentious areas in modern society. For some it protects the rights of individuals to privacy and security, while for others it puts up barriers against the protection of our society. This book aims to develop a deep understanding of cryptography, and provide a way of understanding how privacy, identity provision and integrity can be enhanced with the usage of encryption. The book has many novel features including: • full provision of Web-based material on almost every topic covered • provision of additional on-line material, such as videos, source code, and labs • coverage of emerging areas such as Blockchain, Light-weight Cryptography and Zero-knowledge Proofs (ZKPs) Key areas covered include: • Fundamentals of Encryption • Public Key Encryption • Symmetric Key Encryption • Hashing Methods • Key Exchange Methods • Digital Certificates and Authentication • Tunneling • Crypto Cracking • Light-weight Cryptography • Blockchain • Zero-knowledge Proofs This book provides extensive support through the associated website of: http://asecuritysite.com/encryption
Automatically Generating Cause-and-Effect Questions from Passages

Automated question generation has the potential to greatly aid in education applications, such as online study aids to check understanding of readings. The state-of-the-art in neural question generation has advanced greatly, due in part to the availability of large datasets of question-answer pairs. However, the questions generated are often surface-level and not challenging for a human to answer. To develop more challenging questions, we propose the novel task of cause-and-effect question generation. We build a pipeline that extracts causal relations from passages of input text, and feeds these as input to a state-of-the-art neural question generator. The extractor is based on prior work that classifies causal relations by linguistic category (Cao et al., 2016; Altenberg, 1984). This work results in a new, publicly available collection of cause-and-effect questions. We evaluate via both automatic and manual metrics and find performance improves for both question generation and question answering when we utilize a small auxiliary data source of cause-and-effect questions for fine-tuning. Our approach can be easily applied to generate cause-and-effect questions from other text collections and educational material, allowing for adaptable large-scale generation of cause-and-effect questions.
CenterTrack3D: Improved CenterTrack More Suitable for Three-Dimensional Objects

Abstract Compared with two-dimensional (2D) multi-object tracking (MOT) algorithms, three-dimensional (3D) multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the hm output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach.

We consider the problem of forecasting the daily number of hospitalized COVID-19 patients at a single hospital site, in order to help administrators with logistics and planning. We develop several candidate hierarchical Bayesian models which directly capture the count nature of data via a generalized Poisson likelihood, model time-series dependencies via autoregressive and Gaussian process latent processes, and share statistical strength across related sites. We demonstrate our approach on public datasets for 8 hospitals in Massachusetts, U.S.A. and 10 hospitals in the United Kingdom. Further prospective evaluation compares our approach favorably to baselines currently used by stakeholders at 3 related hospitals to forecast 2-week-ahead demand by rescaling state-level forecasts.
Using Inspiring Novels as Teaching Media to Improve Reading Comprehension of the Second Grade Students of MA Syekh Yusuf Gowa

Based on the result of this research, the researcher suggested that Inspiring Novels was able to use as teaching media in teaching reading comprehension because inspiring novels were easy to get, the story of the novel was appropriate with the students’ age, and sometimes the story of the novel was what they found in their life. The researcher concluded that Using Inspiring Novels as Teaching Media can improve reading comprehension of the second grade students of MASyekh Yusuf Gowa.
Partial and Fully Homomorphic Encryption Schemes for Privacy Preserving

Nowadays security and authentication of user’s data is a major concern over cloud environment. Unauthorized users try to attack outsourced data over the cloud. Different researchers proposed various algorithms based upon data security. Authentication technique such as homomorphic encryption is the latest advancement in privacy preserving. Homomorphic encryption operations are applied to cipher text which provides the similar results that would have been derived from original text. Homomorphic public key cryptography technique preserves user’s data security. This paper focuses on various partial and fully homomorphic encryption schemes and gives a comparative analysis.
Maintenance and other Operating Expenses (MOOE) and School Based Management (SBM) Performance of Secondary Schools in Samar Island

The study investigated the extent of Maintenance and Other Operating Expenses (MOOE) utilization relating to School Based Management (SBM) performance of public secondary schools in Samar Island for school years 2016-2019. A mixed method, explanatory research design was employed utilizing 159 school heads. Focus Group Discussion (FGD) was conducted for selected School heads, disbursing officers or bookkeepers and teacher representatives from six divisions in Samar Island. Quantitatively, this study made use of percentages, weighted mean, and Pearson r for the correlative data; while thematic analysis enriched the qualitative investigation. Findings of this study highlights a high extent of MOOE utilization to ensure students’ access to complete basic education; priority for school ceremonies like the Moving Up of the Junior High School, Closing Ceremonies, and Recognition Programs; to support the needs of the learners for their learning activities; to fund the rentals and minor repairs of tools and equipment that are important to classroom activities; and to fund the expenses related to graduation rites. The results showed that the least priority in the expenses were for the Internet connection of the school, telephone bills, salaries for the janitors, and the security guards. In addition, it was also found out that the SBM performance of the schools have improved in the past three years, from Good to Better. Finally, no substantial connection prevailed between the extent of utilization of MOOE and SBM performance of secondary schools in Samar Island.
The Origin of the Porphyry Deposit Name: From Shellfish, Tyrian Purple Dye, and Imperial Rome to the World’s Largest Copper Deposits

Abstract The porphyry deposit name has a long and fascinating etymological history of over 3,000 years. “Porphyry” is derived from the ancient Greek word porphyra (πoρϕύρα), or purple. It was originally applied to a rare purple dye, Tyrian purple, extracted by the Phoenicians from murex shells. It was later applied to a prized purple porphyritic rock, Imperial Porphyry or Porfido rosso attico, quarried by the Romans from Mons Porphyrites in the Eastern Red Sea hills of Egypt from the first to fifth centuries A.D., and used as a monumental stone in Imperial Rome and Byzantium (Istanbul). The name evolved in the field of igneous petrology to include all rocks with a porphyritic texture, regardless of their color. Mining of the first porphyry copper deposits, which were originally called disseminated or low-grade copper deposits, started in 1905. As a result of the close spatial and genetic relationship to porphyry stocks, they became known as porphyry copper deposits. The term was first used by W. H. Emmons in his 1918 textbook The Principles of Economic Geology, but it was originally used more as an engineering and economic description, as in Parsons’ 1933 book The Porphyry Coppers. It was slow to catch on in the geological literature. It was first used in the title of a paper in Economic Geology in 1947 but did not gain widespread use until the 1970s, following the publication of seminal papers on porphyry models and genesis by Lowell and Guilbert (1970) and Sillitoe (1972, 1973).
Improving Fraud Detection Mechanism in Financial Banking Sectors Using Data Mining Techniques

The banking sector is facing increasing risks of fraud and malpractices. Thus, adopting new methods and approaches to detect, prevent, and predict frauds is essential. Data Mining (DM) techniques are the latest methods which are primarily responsible for ensuring data integrity, when are used in the way of machine learning approach. This paper essentially discusses how can the banking sector takes the advantage of one of these methods is a logistic regression. It is an effective classification algorithm that gives high accuracy for detecting binary problems and through the outcomes it can predict irregular transactions. The paper offers this algorithm effectively by presenting suggested developed model, as a part of a framework for the development of a fraud detection mechanism. Finally, the paper will test the suggested model by using a LR algorithm for proving its performance.
A Novel Spatial FCM-Based Method for Brain MRI Image Segmentation in the Presence of Noise and Inhomogeneity

Unsupervised classification of the brain MRI image into different tissue regions shows salient role to analyze the neuro-imaging and scientific research in the field of medicine and diagnosis. In this paper, an efficient unsupervised spatial fuzzy c-means technique is proposed for classification of distorted brain MRI image with different bias factors. The spatial information has been measured by analyzing three different neighbouring distances to suppress the noise and inhomogeneity as well as preserving the image details at the time of classification. The correlation among the neighbours have been decided by spatial information so that if the centers are itself affected with bias error, it would not affect the correlation. The qualitative and quantitative results are used to state the superiority of the proposed method from the considered methods of interest with reference to the partition coefficient and partition entropy.
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions.

In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings, we introduce RETRO-VIZ, a method for (i) estimating and (ii) explaining trustworthiness of regression predictions. It consists of RETRO, a quantitative estimate of the trustworthiness of a prediction, and VIZ, a visual explanation that helps users identify the reasons for the (lack of) trustworthiness of a prediction. We find that RETRO-scores negatively correlate with prediction error across 117 experimental settings, indicating that RETRO provides a useful measure to distinguish trustworthy predictions from untrustworthy ones. In a user study with 41 participants, we find that VIZ-explanations help users identify whether a prediction is trustworthy or not: on average, 95.1% of participants correctly select the more trustworthy prediction, given a pair of predictions. In addition, an average of 75.6% of participants can accurately describe why a prediction seems to be (not) trustworthy. Finally, we find that the vast majority of users subjectively experience RETRO-VIZ as a useful tool to assess the trustworthiness of algorithmic predictions.