Reviewerly/exHarmony huggingface dataset:
Analysis of Windows OS’s Fragmented File Carving Techniques: A Systematic Literature Review

With the rise in digital crimes nowadays, digital investigators are required to recover and analyse data from various digital resources. Since the files are often stored in fragments owing to memory constraints, the information of the file system and metadata of the file is required to recover the file. However, in cases where the file system is destroyed intentionally or unintentionally, and the metadata is deleted as well, the recovery of the digital evidence is done by a special method known as carving. In file carving, files are recovered solely based on the information about the structure and content of the individual file rather than matching the system’s information of the file. The process of file carving in digital forensics first requires classifying and then arranging the blocks of data that are typically stored as a sequence of bytes in memory. But carving is only possible when the file is not damaged or corrupted otherwise carving is not possible. The aim of this research is to analyse various Windows OS’s file carving techniques used in Digital Forensics particularly for their strengths and weaknesses. This analysis leads to the need of explicitly designed file carvers for different types of files. A novel technique for carving Microsoft’s Word files (a compound format file which is least researched upon) has also been proposed in the document.
Exploring Improvisational Approaches to Social Knowledge Acquisition

To build agents that can engage user in more open-ended social contexts, more and more attention has been focused on data-driven approaches to reduce the requirement of extensive, hand-authored behavioral content creation. However, one fundamental challenge of data-driven approaches, is acquiring human social interaction data with sufficient variety to capture more open-ended social interactions, as well as their coherency. Previous work has attempted to extract such social knowledge using crowdsourced narratives. This paper proposes an approach to acquire the knowledge of social interaction by integrating an improvisational theatre training technique into a crowdsourcing task aimed at collecting social narratives. The approach emphasizes theory of mind concepts, through an iterative prompting process about the mental states of characters in the narrative and paired writing, in order to encourage the authoring of diverse social interactions. To assess the effectiveness of integrating prompting and two-worker improvisation to the knowledge acquisition process, we systematically compare alternative ways to design the crowdsourcing task, including a) single worker vs. two workers authoring interaction between different characters in a given social context, and b) with or without prompts. Findings from 175 participants across two different social contexts show that the prompts and two-workers collaboration could significantly improve the diversity and the objective coherency of the narratives. The results presented in this paper can provide a rich set of diverse and coherent action sequences to inform the design of socially intelligent agents.
Ontology Based Approach for Annotating a Corpus of Computer Science Abstracts

About 2 million new scientific papers are published yearly. This leads to difficulties with extracting information and searching texts. This paper proposes creating “CSAC” annotated corpus of computer science research papers' bibliographic information, such as title and abstract. The corpus uses an XML schema to apply two types of annotations: structuring and semantic annotations. First, IMRaD format is applied for structuring the abstracts. Second, ontology is used to semantically annotate the different properties of algorithms in the abstracts. Applied linguistics is discussed to investigate the possibility of semi-automate the annotations. Since this paper claims that our methodology improves writing and searching the abstracts of computer science research papers, we propose to evaluate these claims via developing an online application based on “CSAC” that allows the users to, first, classify the sentences of their abstracts and, to second, search for specific algorithm using one of is properties. Last, a survey will be conducted to measure the users' satisfaction of using the proposed application.
A Fuzzy-Dynamic Bayesian Network Approach for Inference Filtering

Bayesian Networks (BN) are used for representing and inferring over variables with aleatory uncertainty. Dynamic Bayesian Networks (DBN) extend this concept by introducing temporal dependencies that catch dynamic behaviors from the domain variables. Effective and efficient modeling through BN demands data discretization on categories. However, these categories may have vagueness uncertainty, once are used labels not defined by exact numerical thresholds. Fuzzy Theory provides a framework for modeling vagueness uncertainty. Although hybrid theories to integrate Fuzzy Theory and BN inference process have been proposed, there are still limitations on using fuzzy evidence on DBN. The related works restrict the evidence modeling to the overlapping of only two fuzzy membership functions. Thereby, this work proposes a method for Dynamic Fuzzy-Bayesian inference over non-dichotomic variables. To evaluate the proposal, the model is applied as a classifier on the Detection Occupancy Dataset and compared with other approaches. In the experiments, the model obtained Accuracy 97% and Recall 92%.
Real‐time target object tracking via dual‐directional scaling state predicting

Visual tracking using correlation filters can achieve a high running speed. However, it is still a challenging problem to deal with drastic appearance changes (e.g. large and rapid scale variation of a target) during tracking. In this Letter, a novel dual-directional scaling state predicting method based on correlation filtering is proposed. The adaptive horizontal and vertical searches are adopted to separately and accurately estimate the scale changes in different directions. The online scale to size module is designed to enhance the prediction robustness of the target appearance. Experimental comparisons with several state-of-the-art object trackers show the effectiveness and efficiency of the proposed approach.
High-Performance Architectures for Finite Field Inversion Over GF(2163)

Inversion is the most computationally expensive finite field operation in public-key cryptographic such as elliptic curve cryptography (ECC). This paper presents highperformance architectures for performing the finite field inversion using Gaussian Normal Bases (GNB) and a digit-level serial-in parallel-out multiplier (DL-SIPO) over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ). We propose three architectures to carry out the inversion operation. The first one is based on classic Itoh-Tsujji Algorithm (ITA), the second one carries out the inversion operation according to the NIST binary fields over GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and finally, the last one is based on Fermat's Little Theorem (FLT). The architectures were designed using VHDL description, synthesized on the Stratix IV FPGA using Quartus Prime 17.0, and verified in ModelSim and Matlab. The synthesis results show that the designed architectures present a very good performance using low area. In this case, the processing time and area resources to compute the inversion operation were 114.2, 115.9 and 114.5 ns using 11624, 11558 and 11690 LUTs, respectively.
Using a Hypertext Pre-processor Code in Converting .csv File into a Civil Service Commission Daily Time Record Format Applying Agile Model

The system was designed and developed for the administration, faculty and staff of Isabela State University Cauayan Campus providing an efficient, organized and well-defined Daily Time Record. Aside from that, the software also gives strong security in recording the attendance of the employees due to the device used. The primary objective of the system is to convert the .csv file from the stand-alone face recognition device into a MariaDB database and arranged it into an organized printable output based on the prescribed DTR format of the Civil Service Commission. The proponents have applied the Agile Model (See Fig.1) and utilizes the different developmental platform for developing the project like the MariaDB Database, PHP (Hypertext pre-processor) and Web browser. Also, the Civil Service Commission Daily Time Record Form for designing the printed DTR. Testing was undergone and some errors shown in the printed DTR encountered by some of the employees. It is found out that errors were come from both to the users and to the face recognition device. The reasons behind problems are, users have multiple ID for both the device and the system and users were missed to register their time in their prescribed remark. To be able to have complete data in the DTR, it is suggested that faculty and staff should have one DTR ID and check the device always if it is under time-in or time-out. Despite the problems arisen, the testing still shows the success of the system because the software provides the converted data into a printable and organized output.
Construction of Service-oriented University Institutional Repository Integrating Scientific Research Management

This paper mainly analyzes the problems existed in the construction of university institutional repository and discusses the construction of university institutional repository integrating scientific research management, of this process, taking the characteristics of institutional repository into account. As a result, it can make the institutional scientific research work successfully. Specifically, the paper starts with five modules, discussing the service demand and application which the scientific research management demand for institutional repository, providing a new thinking for the university libraries to build a service-oriented institutional repository.
Quaternion alpha-rooting image enhancement of grayscale images

The proposed method is a new approach for enhancing grayscale images, when the images are map to quaternion space, and then, the quaternion based enhancement technique is used. Namely, the quaternion alpha-rooting method to enhance the so generated “quaternion” image. Currently, there are only very limited techniques to convert a grayscale image to color image, and in this article we propose a novel conversion technique which helps in easily converting a grayscale image to a color or quaternion image. In addition to that, we describe the quaternion alpha-rooting method of quaternion image enhancement. Quaternion approach of enhancement allows for processing the multi-signaled image as a single unit. The fast algorithm of quaternion discrete Fourier transforms makes the implementation of the enhancement method practically possible and effective. The results of image enhancement by the proposed method and comparison with the traditional alpha-rooting of grayscale images are described. The metric used to assess the quality of enhancement shows good values for the results of the proposed enhancement. One of the enhancement metrics is the contrast-based metric referred to as the enhancement measure estimation (EME). Other metrics used to assess the quality of the enhanced images are signal-to-noise ratio (SNR), mean-square-root error (MSRE).
Educational Aspect of Electronic Bookmobile in National Library of Indonesia

The right of the community to obtain education and knowledge is something important to realize. But sometimes the community does not access to right because of difficult in access and affordability to knowledge resources. Electronic bookmobile provides extension services for communities located far from knowledge access. Through a qualitative approach, this research seeks to identify electronic bookmobile in the Indonesian National Library and its values in education. The result is that the realization of the value of education in electronic bookmobile is summarized in several values such as learning to know, learning to do, learning to live together and learning to be. Exposing electronic bookmobile from an educational perspective is a way to understand the broader context of the existence of electronic bookmobile for the community. Educational aspects are not independent but are bound and connected with other elements such as social culture, policies, and services.
The Research on Core Development Technology of Security Big Data Application Platform

Big data is the product of the rapid expansion of Internet data, and the potential of big data analysis has also been stimulated. Many companies will use the advantages of the new technology of big data to enhance their competition. This paper mainly studies the application of big data technology in the security work of large events. The main of this article, through the use of large data technology for integrated excavation, and through the use of application terminals for information collection in large-scale event sites, the identity of the personnel as the excavation entrance, the excavation of the identity of the collected information, crime background information, network relations, etc. With the corresponding software development technology to demonstrate the results of business classification, so as to construct a large data security platform. This paper uses Oracle data warehouse technology, combined with big data technology to construct all kinds of business data into large data layer after extraction, conversion, and filtering, to study and analyze the core development technology of security big data application platform. Using Web development technology to realize face recognition and vehicle recognition.
Ecosystem Description of Hadoop Platform Based on HDFS, MapReduce and Data Warehouse Tool Hive

This paper introduces the processing process of the distributed file system (HDFS, MapReduce) which is the core of the Hadoop distributed computing platform and introduces the data warehouse tool Hive and the distributed database Hbase. Spark is a big data distributed programming framework, which not only implements MapReduce operator map function and reduce function and calculation model, but also provides more abundant operators. This paper describes the ecosystem of Hadoop platform based on HDFS, MapReduce and data warehouse tool Hive.
Identifying Misaligned Spans in Parallel Corpora Using Change Point Detection

Parallel corpora are the basic resource for many multilingual natural language processing models. Recent advances in, e.g. neural machine translation have shown that the quality of the alignment in the corpus has a crucial impact on the quality of the resulting model, renewing interest in filtering automatically aligned corpora to increase their quality. In this contribution, we investigate the use of a fast change point detection method to detect possibly problematic parts of a parallel corpus. We demonstrate its performance on German-English corpora of 11k and 31k sentences, achieve a boundary identification performance above 80% and improve the detection of genuine parallel sentences up to 88%. To our knowledge this is the first application of change point detection to the problem of error detection in noisy corpora.
Using embedded operating system as a modular provisioning platform for IP telephony

This article deals with the system for modular provisioning the IP telephony devices. Theoretical part deals with issues in mass configuration and data synchronization, followed by the exact practical implementations of the provisioning solutions. The article also describes the design and implementation of the whole platform, including the subsequent testing of functionality with possible design of further improvements.
Social Marketplace Monitoring and Sentiment Analysis

The overwhelming majority of existing approaches to opinion feature extraction trust mining patterns for one review corpus, ignoring the nontrivial disparities in word spacing characteristics of opinion options across completely different corpora. During this research a unique technique to spot opinion options from on-line reviews by exploiting the distinction in opinion feature statistics across two corpora, one domain-specific corpus (i.e., the given review corpus) and one domain-independent corpus (i.e., the contrasting corpus). The tendency to capture this inequality called domain relevance (DR), characterizes the relevancy of a term to a text assortment. The tendency to extract an inventory of candidate opinion options from the domain review corpus by shaping a group of grammar dependence rules. for every extracted candidate feature, to have a tendency to estimate its intrinsic-domain relevancy (IDR) and extrinsic-domain relevance(EDR) scores on the domain-dependent and domain-independent corpora, severally. Natural language processing (NLP) refers to computer systems that analyze, attempt understand, or produce one or more human languages, such as English, Japanese, Italian, or Russian. Process information contained in natural language text. The input might be text, spoken language, or keyboard input. The field of NLP is primarily concerned with getting computers to perform useful and interesting tasks with human languages. The field of NLP is secondarily concerned with helping us come to a better understanding of human language
Электронные образовательные ресурсы в обучении цветоведению

The article considers the issues of design and content of modern learning electronic educational resources, lists the advantages and negative factors regarding the use of these resources in the learning process. Achievements of the advantages and minimization of the negative effects of electronic resources in the modern educational space examines the discipline "Pedagogical design", offering the tools necessary to develop effective, attractive and understandable for students of digital educational materials. Color science "for future artists-teachers.
Pruning External Minimality Checking for ASP Using Semantic Dependencies

hex-programs integrate external computations in ASP. For hex-evaluation, an external (e)-minimality check is required to prevent cyclic justifications via external sources. As the check is a bottleneck in practice, syntactic information about atom dependencies has been used previously to detect when the check can be avoided. However, the approach largely overapproximates the real dependencies due to the black-box nature of external sources. We show how the dependencies can be approximated more closely by exploiting semantic information, which significantly increases pruning of e-minimality checking. Moreover, we analyze checking and optimization of semantic dependency information. An empirical evaluation exhibits a clear benefit of this approach.
Enhanced sensing through multiphoton derived hyper-entanglement and networks

Methods of improving quantum LADARs and related sensors are developed based on quantum entanglement and hyperentanglement. Multiple single photon states are used to obtain a multiphoton entangled state. These states can be N00N states, M&amp;N states (M&amp;N), or a linear combination of M&amp;N states (LCMNS) or generalized states kindred to LCMNS. The procedure for doing this derives from the fundamental theorem of algebra. Various states generated by this process are developed. A diagram of a device for producing such states is examined. They are related to a simpler version of this concept introduced in the seminal experiment by Mitchell-Lundeen and Steinberg. A certain class of states obtained through the Schrodinger kitten process are shown to be effective for generating states hyper-entangled in polarization and energy-time. A diagram of a device for producing such states is considered. Alternate methods of generating hyperentanglement in polarization and energy-time are discussed. Improvements offered by networks are discussed. The utility of these procedures for sensing and communications is examined. An open systems analysis based on density operator theory is conducted including both noise and loss mechanisms. The susceptibility to noise and loss of the various hyper-entanglement procedures is examined. Various measures of effectiveness (MOEs) are derived to quantize system performance. MOEs include but are not limited to, SNR, signal-to-interference ratio, quantum Cramer Rao’ lower bound, quantum Chernoff bound, measurement time, the Holevo bound, sensing range, and resolution. A summary table with expanded MOEs results drawing from multiple papers is provided.
Evaluation Based on Adaptive Online Testing

This article discusses the role of evaluation in the educational process. The article also clarifi ed the features of electronic assessment and its functions. The authors focus on the use of adaptive testing and creating a block adaptive test using Google Forms.
Testing a stylometric tool in the study of Middle English documentary texts

This article is about testing Stylo, a stylometric script for R, in the study of Middle English documents. The main aim of the paper is to be able to discriminate between Middle English documents and document groups with the help of an automatic classification script. The basic assumption is that stylometric tools, and Stylo in particular, may provide new insights into affinity between Middle English texts. In other words, stylometric tools in general help to identify text groups the members of which have something in common in their parentage; Stylo for R is no exception in this.
Abstraction in Modeling and Programming with Associations: Instantiation, Composition and Inheritance

The association concept abstracts over collaboration between concurrent autonomous entities. Associations describe collaborations, i.e., requests and coordination of contributions from the entities. And the association concept also supports abstraction in the form of instantiation, composition and inheritance. Simple and expressive abstraction with associations is demonstrated by illustrative examples, schematic language presentations, brief characterizations of qualities and implementation experiments.
Hybrid CNNs

With the prevalence of virtual reality, augmented reality and autonomous robots, the high resolution spherical images they produced make the standard convolutional neural networks (CNNs), which have been proven powerful on perspective images, non-trivial.
Scrum Towards IRAM-ISO 9001:2015. Integrating Documentation Required

On the road to achieving good process quality management in software-developing small and medium enterprises, there is a lack of documentation in methodologies that are currently very widely used (such as Scrum). A proposal to adapt Scrum documentation and recommended IEEE standards for development process stages to the documentation required by IRAM-ISO 9001:2015, defining a single integrating document, is presented.
Implicit Recommendation with Interest Change and User Influence

Aiming at the problem of rich websites in campus without targeted recommendation, which makes it difficult for users to find the information resources of high interest and high quality, this paper proposes an implicit feedback recommendation algorithm in campus network based on user's changing interest and user influence. Based on the traditional collaborative filtering algorithm, introduces time function that adapting to user's changing interest and user's influence factors. The score matrix based on time weight is integrated with the influence matrix to solve the problem that user similarity calculation is too single, and improves the accuracy and explanatory of the recommendation results. Experimental results show that the algorithm can effectively reduce the sparsity and cold start problem of the dataset, and has better recommendation quality than traditional collaborative filtering algorithm.