Reviewerly/exHarmony huggingface dataset:
The Research of Data Recovery on Windows File Systems

With the rapid development of information technology, computer information security issues are becoming more and more concerned. At present, the market share of Windows operating system exceeds 92%. FAT32 and NTFS are the popular file systems on Windows operating system. Aiming at the data loss on Windows file systems, the data recovery schemes on both FAT32 and NTFS file systems are proposed. First of all, the file system structure on FAT32 is introduced. The FAT table of FAT32 system is analyzed. The recovery e processes of DBR, deleted files and formatted files on FAT32 are elaborated. Next, the file system structure on NTFS is introduced. The main file allocation table of NTFS system is analyzed. According to the different storage characteristics of file record, file attribute and run list on NTFS, the specific processes of data recovery are elaborated. By comparison, the NTFS system is easier to find files and recover files due to its advantages in structure and file storage mechanism. It turns out that the method in the article has proven to be effective.
Working Memory Graphs

Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences. Inspired by this trend, we study the question of how Transformer-based models can improve the performance of sequential decision-making agents. We present the Working Memory Graph (WMG), an agent that employs multi-head self-attention to reason over a dynamic set of vectors representing observed and recurrent state. We evaluate WMG in three environments featuring factored observation spaces: a Pathﬁnding environment that requires complex reasoning over past observations, BabyAI gridworld levels that involve variable goals, and Sokoban which emphasizes future planning. We ﬁnd that the combination of WMG’s Transformer-based architecture with factored observation spaces leads to signiﬁcant gains in learning efﬁciency compared to baseline architectures across all tasks. WMG demonstrates how Transformer-based models can dramatically boost sample efﬁciency in RL environments for which observations can be factored.
Technology as a tool to enhance motivation and learning

The aim of this work is to think about how technologies can have a role in motivation for learning. To respond to the necessary paradigm shift school faces, resulting from a different way of life in very recent past, the use of technology proves to be a powerful ally, ensuring student’s ability to respond to technical and scientific questions as well as creating an environment of trust and security in classroom. Considering that we are at a critical turning point in technological evolution and changes seem irreversible. What new challenges does the teacher also face in this field? To understand what makes the activity and the class remarkable for students, we are using semi-structured interview, focus group and questionnaire. Methods and technics used in the classes were diversified, and according to the intended goal. The use of these methods was not conditioned by technology but by the defined learning goals. Although this work is still beginning and needs to be continued, the results show that technology helps students to feel confident since it doesn’t expose individual (un)knowledge, allows them to learn with pleasure, generates group dynamics and globally, creates happy moments that increase motivation and improve learning. Results show us that it is imperative to realize how much the teacher as an important integrative agent mediating between the real/virtual, technological/scientific, curriculum/skill, cognitive/emotional worlds.
Generalization to New Actions in Reinforcement Learning

A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances, such as making decisions from new action choices. However, standard reinforcement learning assumes a fixed set of actions and requires expensive retraining when given a new action set. To make learning agents more adaptable, we introduce the problem of zero-shot generalization to new actions. We propose a two-stage framework where the agent first infers action representations from action information acquired separately from the task. A policy flexible to varying action sets is then trained with generalization objectives. We benchmark generalization on sequential tasks, such as selecting from an unseen tool-set to solve physical reasoning puzzles and stacking towers with novel 3D shapes. Videos and code are available at this https URL
Interaction is necessary for distributed learning with privacy or communication constraints

Local differential privacy (LDP) is a model where users send privatized data to an untrusted central server whose goal it to solve some data analysis task. In the non-interactive version of this model the protocol consists of a single round in which a server sends requests to all users then receives their responses. This version is deployed in industry due to its practical advantages and has attracted significant research interest.
Automatic Reparameterisation of Probabilistic Programs

Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data. However, the performance of inference algorithms can be dramatically affected by the parameterisation used to express a model, requiring users to transform their programs in non-intuitive ways. We argue for automating these transformations, and demonstrate that mechanisms available in recent modeling frameworks can implement non-centring and related reparameterisations. This enables new inference algorithms, and we propose two: a simple approach using interleaved sampling and a novel variational formulation that searches over a continuous space of parameterisations. We show that these approaches enable robust inference across a range of models, and can yield more efficient samplers than the best fixed parameterisation.
Improved learning accessibility and professionalism of teachers in remote areas through mentoring development of teaching materials based on Augmented Reality

The problem that underlies the Augmented Reality-based teaching material development assistance program for teachers in remote areas is located in the gap in the progress of the quality of education between remote and urban areas caused by demanding access to cities, and the lack of knowledge and understanding of teachers in developing materials innovative teaching by utilizing current technological advancements. Community service activities aim to produce innovative teaching materials based on Augmented Reality at Muhammadiyah 4 Daun Vocational School and MTs Muhammadiyah 5 Daun Sangkapura, Bawean Island. Bawean Island is located 80 miles or 135 km north of Java. The geographical location and transportation conditions connecting Bawean and Gresik islands are the main obstacles for teachers on Bawean island to follow the development of science and technology which results in the low quality of the learning process so that it lags behind the development of information technology.&#x0D; Form of implementation of the program as a realization troubleshooting manifest in periodic and scheduled activities through a strengthening phase followed the concept of mobile learning needs analysis workshop, workshop material development, and implementation with the assistance of a team of stewards and continued application independently by the school in the implementation phase of using the steps Lesson Study as much as two cycles. The method used in the implementation of this activity is the method of discussion, workshops, and direct practice in class. Increased knowledge and ability of teachers teaching materials related to the development of innovative Augmented Reality-based and capable of producing the best practices in the implementation report the development of innovative teaching materials based Augmented Reality in learning in SMK Muhammadiyah 4 Daun and MTs Muhammadiyah 5 Daun Sangkapura, Bawean
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models

One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i.e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i.e. generalization). In contrast, unsupervised settings have been under-explored, despite the fact that it was observed that overparameterization can be helpful as early as Dasgupta & Schulman (2007). We perform an empirical study of different aspects of overparameterization in unsupervised learning of latent variable models via synthetic and semi-synthetic experiments. We discuss benefits to different metrics of success (recovering the parameters of the ground-truth model, held-out log-likelihood), sensitivity to variations of the training algorithm, and behavior as the amount of overparameterization increases. We find that across a variety of models (noisy-OR networks, sparse coding, probabilistic context-free grammars) and training algorithms (variational inference, alternating minimization, expectation-maximization), overparameterization can significantly increase the number of ground truth latent variables recovered.
Improvement of Menezes-Vanstone Elliptic Curve Cryptosystem Based on Quadratic Bézier Curve Technique

Cryptography is one of the most important applications and widely used in our life especially in the information security that needed by many government institutions, banks, communications and others to keep data over internet and other transportations that it is ensure safety of transfers between the sender and the recipient.The most important system in cryptography is public key cryptography and the mostly used is the elliptic curves cryptosystem, because of it is very efficient and secure and difficult to solve the discrete logarithm problem and find the secret key.In this study a new method is introduced using the Menezes-Vanstone Elliptic Curve Cryptosystem (MVECC) for data based on quadratic Bézier curve techniques.The purpose of this proposal is to increase the security of this cryptosystem.We will apply this proposed method to all measurements of National Institute of Standards and Technology (NIST) tests and running time and compared it with the original method.
VERBAL AND NON-VERBAL CREATIVITY AND TOLERANCE &#x0D; FOR UNCERTAINTY OF PSYCHOLOGICAL STUDENTS

The article contains results of diagnostics of creativity and tolerance to the uncertainty of psycholo-gy students. An analysis of statistically significant differences in creativity (originality and uniqueness) in students with different levels of uncertainty tolerance showed that students with an average level of to-lerance for uncertainty have higher creativity rates than students with a high level of tolerance for uncer-tainty. On the one hand, these students are characterized by a certain ability to perceive new, ambi-guous, unstructured situations, not as a threatening factor, but as a challenge-containing situation, with-out experiencing pronounced negative emotions. On the other hand, these students are characterized by optimal emotional inclusion, increased activation, a desire to overcome uncertainty, as well as a certain, constructive “intolerance” to uncertainty. This contributes to the production of attempts to solve the situ-ation, find a solution to the problem, activates the creative potential of the person.
Multi Label Ranking Based on Positive Pairwise Correlations Among Labels

Multi-Label Classification (MLC) is a general type of classification that has attracted many researchers in the last few years. Two common approaches are being used to solve the problem of MLC: Problem Transformation Methods (PTMs) and Algorithm Adaptation Methods (AAMs). This Paper is more interested in the first approach; since it is more general and applicable to any domain. In specific, this paper aims to meet two objectives. The first objective is to propose a new multi-label ranking algorithm based on the positive pairwise correlations among labels, while the second objective aims to propose new simple PTMs that are based on labels correlations, and not based on labels frequency as in conventional PTMs. Experiments showed that the proposed algorithm overcomes the existing methods and algorithms on all evaluation metrics that have been used in the experiments. Also, the proposed PTMs show a superior performance when compared with the existing PTMs
A brief study on Software as a Service in Cloud Computing Paradigm

Software as a Service (SaaS) is a paradigm that provides end users or clients easy and seamless access to various applications, using the Internet without requiring any infrastructure or related software. To virtualize the access to applications and functionalities, SaaS providers use the cloud-computing environment to rent resources, thereby reducing both the capital and operational expenditure. SaaS uses the cloud-computing infrastructure to distribute applications to many users, irrespective of their location or infrastructure capacity. This one-to-many model with centralized control has the potential to transform the behavior of traditional IT architecture, its pricing, partnering, and management. With the emergence of SaaS as a delivery system, the whole software environment is moving toward a producer-consumer system, where both are distributed globally. We point out the motivation for accepting the new technologies, such as to reduce the expense, energy consumption, maintenance, etc. We also point out the challenges and risks associated with the paradigm shift. The present study categorically presents state-of-the-art research on the security issues of collaborative SaaS cloud computing and integrating service-level-agreement and quality-of-service-related issues of SaaS in cloud-computing environments.
Proof Learning in PVS With Utility Pattern Mining

Interactive theorem provers (ITPs) are software tools that allow human users to write and verify formal proofs.In recent years, an emerging research area in ITPs is proof mining, which consists of identifying interesting proof patterns that can be used to guide the interactive proof process in ITPs.In previous studies, some data mining techniques, such as frequent pattern mining, have been used to analyze proofs to find frequent proof steps.Though useful, such models ignore the facts that not all proof steps are equally important.To address this issue, this paper proposes a novel proof mining approach based on finding not only frequent patterns but also high utility patterns to find proof steps of high importance (utility).A proof process learning approach is proposed based on high utility itemset mining (HUIM) for the PVS (Prototype Verification System) proof assistant.Proofs in PVS theories are first abstracted to a computer-processable corpus, where each line represents a proof sequence and proof commands in proof sequences are associated with utilities representing their weightage (importance).HUIM techniques are then applied on the corpus to discover frequent proof steps/high utility patterns and their relationships with each other.Experimental results suggest that combining frequent pattern mining techniques, such as sequential pattern mining and high utility itemset mining, with proof assistants, such as PVS, is useful to learn and guide the proof development process.
WhatsApp Chat Visualizer: A Visualization of WhatsApp Messenger’s Artifact Using the Timeline Method

&lt;p&gt;WhatsApp is a medium that everyone can use to interact and to share information effectively and efficiently. However, it can be misused for criminal activities. Analyzing WhatsApp' artifacts is quite challenging as the suspect may have a lot of random conversational data to be considered. This makes it difficult for the trial process to obtain digital traces that can be identified in malicious activities such as knowing who was involved when the conversation was held and the timespan. Therefore, in this research, a social media investigation of WhatsApp was carried out by acquiring data from rooted Android devices that were used as target devices for forensic activities. A python-based application is developed to show the content of the conversation, and a web-based application is presented to visualize the data using the Timeline method. Experimental results in this research display important timeline information such as information about who was involved when and what time the conversation was carried out with the suspect.&lt;/p&gt;
Unsupervised out-of-distribution detection using kernel density estimation

Deep neural networks achieve significant advancement to the state-of-the-art in many computer vision tasks. However, accuracy of the networks may drop drastically when test data come from a different distribution than training data. Therefore, detecting out-of-distribution (OOD) examples in neural networks arises as a crucial problem. Although, majority of the existing methods focuses on OOD detection in classification networks, the problem exist for any type of networks. In this paper, we propose an unsupervised OOD detection method that can work with both classification and non-classification networks by using kernel density estimation (KDE). The proposed method estimates probability density functions (pdfs) of activations at various levels of the network by performing KDE on the in-distribution dataset. At test time, the pdfs are evaluated on the test data to obtain a confidence score for each layer which are expected to be higher for in-distribution and lower for OOD. The scores are combined into a final score using logistic regression. We perform experiments on 2 different classification networks trained on CIFAR-10 and CIFAR-100, and on a segmentation network trained on Pascal VOC datasets. In CIFAR-10, our method achieves better results than the other methods in 4 of 6 OOD datasets while being the second best in the remaining ones. In CIFAR-100, we obtain the best results in 2 and the second best in 3 OOD datasets. In the segmentation network, we achieve the highest scores according to most of the evaluation metrics among all other OOD detection methods. The results demonstrate that the proposed method achieves competitive results to the state-of-the-art in classification networks and leads to improvement on segmentation network.
Data Science Tools for Monitoring the Global Repository Eco-System and its Lines of Evolution

The global network of scholarly repositories for the publication and dissemination of scientific publications and related materials can already look back on a history of more than twenty years. During this period, there have been many developments in terms of technical optimization and the increase of content. It is crucial to observe and analyze this evolution in order to draw conclusions for the further development of repositories. The basis for such an analysis is data. The Open Archives Initiative (OAI) service provider Bielefeld Academic Search Engine (BASE) started indexing repositories in 2004 and has collected metadata also on repositories. This paper presents the main features of a planned repository monitoring system. Data have been collected since 2004 and includes basic repository metadata as well as publication metadata of a repository. This information allows an in-depth analysis of many indicators in different logical combinations. This paper outlines the systems approach and the integration of data science techniques. It describes the intended monitoring system and shows the first results.
NaLa-Search: A multimodal, interaction-based architecture for faceted search on linked open data

Mobile devices are the technological basis of computational intelligent systems, yet traditional mobile application interfaces tend to rely only on the touch modality. That said, such interfaces could improve human–computer interaction by combining diverse interaction modalities, such as visual, auditory and touch. Also, a lot of information on the Web is published under the Linked Data principles to allow people and computers to share, use and/or reuse high-quality information; however, current tools for searching for, browsing and visualising this kind of data are not fully developed. The goal of this research is to propose a novel architecture called NaLa-Search to effectively explore the Linked Open Data cloud. We present a mobile application that combines voice commands and touch for browsing and searching for such semantic information through faceted search, which is a widely used interaction scheme for exploratory search that is faithful to its richness and practical for real-world use. NaLa-Search was evaluated by real users from the clinical pharmacology domain. In this evaluation, the users had to search and navigate among the DrugBank dataset through voice commands. The evaluation results show that faceted search combined with multiple interaction modalities (e.g. speech and touch) can enhance users’ interaction with semantic knowledge bases.
Towards Adversarially Robust Knowledge Graph Embeddings

Knowledge graph embedding models enable representation learning on multi-relational graphs and are used in security sensitive domains. But, their security analysis has received little attention. I will research security of these models by designing adversarial attacks against them, improving their adversarial robustness and evaluating the effect of proposed improvement on their interpretability.
Certified Unsolvability for SAT Planning with Property Directed Reachability

While classical planning systems can usually detect if a task is unsolvable, only recent research introduced a way to verify such a claim. These methods have already been applied to a variety of explicit and symbolic search algorithms, but so far no planning technique based on SAT has been covered with them. We fill this gap by showing how property directed reachability can produce proofs while only minimally altering the framework by allowing to utilize certificates for unsolvable SAT queries within the proof. We additionally show that a variant of the algorithm that does not use SAT calls can produce proofs that fit into the existing framework without requiring any changes.
Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance

The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.