Reviewerly/exHarmony huggingface dataset:
Digital Evidence and Prohibitions of Evidence Evaluation

Since the first moment of the history of humanity, various means of evidence and evidence have been used to reach and detect the offender. As crime types and means of crime have changed and evolved, evidence detection and analysis methods have changed over time. At this point, with the rapid advancement of technology, the emergence of various and new types of crime that can affect many people at the same time in the global world is inevitable. Wherever you are in the world, it is now possible to commit crimes in one way or another that affect one or more people on the other side of the world through the Internet and digital systems. This, in turn, has led states to safeguard their cyber security. For this reason, firstly, it has made a legal regulation to protect its own citizens in domestic law and then it has been forced to cooperate internationally. The way in which classic crime types are committed and the method of evidence is different from the crimes committed through digital systems. The main purpose of the methods of obtaining and proving evidence is to reveal the material truth for a past event. There are also legal requirements, technical methods and scientifically accepted methodologies that must be followed when uncovering material truth. Because digital evidence is both different and more open to manipulation than other classical proofs, it is a matter of adherence to laws and technical rules when obtaining evidence. In order to obtain digital evidence in the Code of Criminal Procedure No. 5271; It has been arranged as a search, copy and seizure protection measure in computers, computer programs and files. Although the heading of the respective substance is initially considered only as a protection measure for computers, it will find application for all devices and systems containing digital data, as detailed below. Received: 5 June 2021 / Accepted: 15 July 2021 / Published: 5 September 2021
How non-designers understand the architecture design project: a comparative study using immersive virtual reality

This study focusses on how people who are not familiar with the traditional methods used to represent architecture understand architecture design projects. The main objective of the study was to assess the client’s level of comprehension of a design project presented using traditional representation methods in comparison with a method based on the use of immersive virtual reality. Two experiments were conducted, involving architects, design projects and real clients. In both experiments an architecture design project was presented using two different methods: i) the traditional method, ii) the immersive method, using virtual reality, viewed by means of a Head Mounted Display. The results were used to address the questions posed during the study regarding comprehension of architecture design projects. They confirmed the hypothesis that individuals who are not familiar with architectural representations have a better and more accurate perception of a project after viewing it using immersive virtual reality rather than looking at technical drawings, renders and scale models.
Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension

Model-complete text comprehension aims at interpreting a natural language text with respect to a semantic domain model describing the classes and their properties relevant for the domain in question.Solving this task can be approached as a structured prediction problem, consisting in inferring the most probable instance of the semantic model given the text.In this work, we focus on the challenging subproblem of cardinality prediction that consists in predicting the number of distinct individuals of each class in the semantic model.We show that cardinality prediction can successfully be approached by modeling the overall task as a joint inference problem, predicting the number of individuals of certain classes while at the same time extracting their properties.We approach this task with probabilistic graphical models computing the maximum-aposteriori instance of the semantic model.Our main contribution lies on the empirical investigation and analysis of different approximative inference strategies based on Gibbs sampling.We present and evaluate our models on the task of extracting key parameters from scientific full text articles describing pre-clinical studies in the domain of spinal cord injury.
On solutions of fundamental problems of physics and the contours of new physics

In quantum and gravitational physics, a large set of fundamental problems have accumulated over the past hundred years, which shows the incompleteness of the formation of basic theories.In the paper solutions a number of such problems, presented by the author recently, is reviewed.Unlike other hypothesis-based attempts, the new solutions are based on physical principles and empirical facts, which makes the new formulations of basic theories more consistent.Problems of a "technical" nature, arising due to deviations from known principles, are solved by consistently following them.Other problems were solved by introducing new principles or changing old ones, but also on the basis of facts.Consequently, these solutions in this or a more refined form will enter the new physics initiated by them.The contours and prospects of this new physics are considered.The reasons of the long time crisis in fundamental physics and ways to overcome it are discussed.It is shown that the main reason for the crisis were deviations from the methodology and ethics of natural sciences, and the new physics began to form as a result of the return to them.
Substance over Style: Document-Level Targeted Content Transfer

Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints.Although sentence-level rewriting has been fairly well-studied, little work has addressed the challenge of rewriting an entire document coherently.In this work, we introduce the task of document-level targeted content transfer and address it in the recipe domain, with a recipe as the document and a dietary restriction (such as vegan or dairy-free) as the targeted constraint.We propose a novel model for this task based on the generative pretrained language model (GPT-2) and train on a large number of roughly-aligned recipe pairs. 1 Both automatic and human evaluations show that our model out-performs existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document.Finally, we analyze our model's rewrites to assess progress toward the goal of making language generation more attuned to constraints that are substantive rather than stylistic.
Preference-based Reinforcement Learning with Finite-Time Guarantees

Preference-based Reinforcement Learning (PbRL) replaces reward values in traditional reinforcement learning by preferences to better elicit human opinion on the target objective, especially when numerical reward values are hard to design or interpret. Despite promising results in applications, the theoretical understanding of PbRL is still in its infancy. In this paper, we present the first finite-time analysis for general PbRL problems. We first show that a unique optimal policy may not exist if preferences over trajectories are deterministic for PbRL. If preferences are stochastic, and the preference probability relates to the hidden reward values, we present algorithms for PbRL, both with and without a simulator, that are able to identify the best policy up to accuracy $\varepsilon$ with high probability. Our method explores the state space by navigating to under-explored states, and solves PbRL using a combination of dueling bandits and policy search. Experiments show the efficacy of our method when it is applied to real-world problems.
Comparison of classical and CNN-based detection techniques for state estimation in 2D

Tracking the state of a moving object is a challenging but useful application of computer vision. The problem is two-fold: efficiently and effectively locating the object within an image and derivi...
Determination of Optimal Solar Power and Corresponding Tilted Angle in Different Geoclimatic Zones in Nigeria

Aims: To estimate the optimum tilt angle and maximum solar power for different geoclimatic zones in Nigeria.&#x0D; Methodology: In this study, the surface data of direct and diffuse solar radiations spanning 2013 to 2017 were obtained from the archives of the Modern-Era Retrospective Analysis for Research and Application for 20 stations spread across Nigeria. To study the direct relationship between the solar power on a photovoltaic panel and the solar radiation, the values of the direct, diffuse, and ground-reflected components of solar radiation were calculated for six tilt angles ranging from 15° to 90° using anisotropic models. Afterwards, the maximum values of the solar power and the corresponding tilt angles, as well as the average number of solar panels that can be used to cater for the electricity needs of households in all the geoclimatic zones, were estimated.&#x0D; Results: Analyses showed that the maximum solar power was received at tilt angles of 30° and 45° from October till February, and tilt angles of 15° and 30° from March till September in all the study locations. For instance, the Sahel and Guinea Savanna zones had the highest solar power, whereas Derived Savanna and Coastal zones had relatively low power. Based on the results, it was determined that the number of solar panels required in the sahel zone would be lower than those of the other zones.&#x0D; Conclusion: It can be inferred from the results that the reception of maximum solar power is at the tilt angle of 45o in Sahel, Guinea, and Derived Savanah regions, and at 30o in the Coastal region. The results of this research will provide solar engineers with accurate information on the orientation and tilt of PV modules for efficient power generation.
Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation

The standard neural machine translation model can only decode with the same depth configuration as training.Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly.Such individual training leads to increased model maintenance costs and slower model iterations, especially for the industry.In this work, we propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference.Experimental results show that our approach can simultaneously support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method--LayerDrop.
Supporting Process Mining with Recovered Residual Data

Understanding how workflows are actually carried out within an organization can provide a crucial contribution to business process improvement. This paper presents a concept for reconstructing a business process by using file residuals on a hard-drive and without the need for existing event logs. Thereby, methods from the area of process mining are enriched with approaches from digital forensics investigations in a Digital Trace Miner. First, a framework that extracts traces originating from business process execution based on residual data is developed in order to link them to the processes. The traces from the extraction are used in a life-cycle to keep related data up-to-date. This approach has been implemented and evaluated by a prototype. The evaluation shows that this approach enables useful insights regarding the tasks performed on a suspect computer by associating recovered files by using file-carving mechanisms.
A Group Law on the Projective Plane with Applications in Public Key Cryptography

We present a new group law defined on a subset of the projective plane $\mathbb{F}P^2$ over an arbitrary field $\mathbb{F}$, which lends itself to applications in Public Key Cryptography, in particular to a Diffie-Hellman-like key agreement protocol. We analyze the computational difficulty of solving the mathematical problem underlying the proposed Abelian group law and we prove that the security of our proposal is equivalent to the discrete logarithm problem in the multiplicative group of the cubic extension of the finite field considered. Finally, we present a variant of the proposed group law but over the ring $\mathbb{Z}/pq\mathbb{Z}$, and explain how the security becomes enhanced, though at the cost of a longer key length.
Parsing Gapping Constructions Based on Grammatical and Semantic Roles

A gapping construction consists of a coordinated structure where redundant elements are elided from all but one conjuncts.This paper proposes a method of parsing sentences with gapping to recover elided elements.The proposed method is based on constituent trees annotated with grammatical and semantic roles that are useful for identifying elided elements.Our method outperforms the previous method in terms of F-measure and recall.
Artificial intelligence: From electronics to optics

Machine Learning and big data are currently revolutionizing our way of life, in particular with the recent emergence of deep learning. Powered by CPU and GPU, they are currently hardware limited and extremely energy intensive. Photonics, either integrated or in free space, offers a very promising alternative for realizing optically machine learning tasks at high speed and low consumption. We here review the history and current state of the art of optical computing and optical machine learning.
THE IMPLEMENTATION OF DUOLINGO TO TEACH DESCRIPTIVE TEXT IN SCIENTIFIC APPROACH

Reading is a process of communication. The process of reading can be stated through direct communication or by writing as well imagination of the reader that can be expressed in daily activities. This article discussed how to apply the Duolingo application using techniques that apply a scientific approach method in teaching reading process. The subject of this research consist of 38 students. The aim of this article was to find out the extent of the students understanding in reading seeing the right words and readings that are in the application. Descriptive qualitative was used in this research. Based on the result, Duolingo application that applied in learning descriptive text can help the students in learning process. Keywords: Duolingo, Reading, Descriptive Text, Scientific Approach
KLearn: Background Knowledge Inference from Summarization Data

The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver.So far, summarization researchers have given considerably more attention to relevance than to background knowledge.In contrast, this work puts background knowledge in the foreground.Building on the realization that the choices made by human summarizers and annotators contain implicit information about their background knowledge, we develop and compare techniques for inferring background knowledge from summarization data.Based on this framework, we define summary scoring functions that explicitly model background knowledge, and show that these scoring functions fit human judgments significantly better than baselines.We illustrate some of the many potential applications of our framework.First, we provide insights into human information importance priors.Second, we demonstrate that averaging the background knowledge of multiple, potentially biased annotators or corpora greatly improves summary-scoring performance.Finally, we discuss potential applications of our framework beyond summarization.
Contribution of Vocabulary Mastery on News Writing Skill

This research is motivated by the results of observations during journalism lectures.When writing news, vocabulary mastery is required.Mastery of vocabulary is important in writing skills, including writing news.In fact, it is not yet known the level of contribution of vocabulary mastery to news writing skills.Mastery of vocabulary is important for one's writing material.Starting from this, this study intends to explain the contribution of vocabulary mastery to students' news writing skills.This study uses a quantitative approach with a distributional correlational method.The population of this study was students of the 2014 FBS UNP Indonesian Language and Literature Study Program.The sample consisted of 51 people taken by a simple random technique using the Taro Yamane formula with a precision of 10%.The data were collected using a news writing skill test and a vocabulary mastery test.Based on the analysis of the research data, it is known that vocabulary mastery contributes to news writing skills.Thus, the results of this study have implications for learning news writing skills.If vocabulary mastery and journalistic knowledge are not considered in learning, student news writing will be less good.So, it is suggested to some parties such as students to increase their vocabulary mastery; for lecturers/teachers to build student vocabulary through increasing reading assignments.
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval

Despite recent success in neural task-oriented dialogue systems, developing such a realworld system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms.To alleviate the above problem, we propose AirConcierge, an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries.Specifically, the neural agent first learns to ask and confirm the customer's intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs.With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs.We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent.The experimental results show that AirConcierge significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.
Persian Handwritten Digit, Character, and Words Recognition by Using Deep Learning Methods.

Digit, character, and word recognition of a particular script play a key role in the field of pattern recognition. These days, Optical Character Recognition (OCR) systems are widely used in commercial market in various applications. In recent years, there are intensive research studies on optical character, digit, and word recognition. However, only a limited number of works are offered for numeral, character, and word recognition of Persian scripts. In this paper, we have used deep neural network and investigated different versions of DensNet models and Xception and compare our results with the state-of-the-art methods and approaches in recognizing Persian character, number, and word. Two holistic Persian handwritten datasets, HODA and Sadri, have been used. For a comparison of our proposed deep neural network with previously published research studies, the best state-of-the-art results have been considered. We used accuracy as our criteria for evaluation. For HODA dataset, we achieved 99.72% and 89.99% for digit and character, respectively. For Sadri dataset, we obtained accuracy rates of 99.72%, 98.32%, and 98.82% for digit, character, and words, respectively.
Cascaded Text Generation with Markov Transformers

The two dominant approaches to neural text generation are fully autoregressive models, using serial beam search decoding, and non-autoregressive models, using parallel decoding with no output dependencies. This work proposes an autoregressive model with sub-linear parallel time generation. Noting that conditional random fields with bounded context can be decoded in parallel, we propose an efficient cascaded decoding approach for generating high-quality output. To parameterize this cascade, we introduce a Markov transformer, a variant of the popular fully autoregressive model that allows us to simultaneously decode with specific autoregressive context cutoffs. This approach requires only a small modification from standard autoregressive training, while showing competitive accuracy/speed tradeoff compared to existing methods on five machine translation datasets.
Applying Hassle Lines Strategy to Improve the Students’ Speaking Skill

This article was to improve the students’ speaking skill at the eleventh grade at sate senior high school 10 Bandar Lampung through Hassle Lines Strategy. Classroom Action Research was used in this method, with the sample of twenty six students that consist of 8 male and 18 female.In cycle of Hassle Lines Strategy, 81% the students prepare the conversation actively, 92% students practice orally, 65% students can mention the body language, and 85% students share to the teacher. From questionnaire, the researcher got the data Hassle Lines Strategy implementing was 91% of student agree and 9% of student disagree, the positive respond accomplished. From the test, 81% of students passed the KKM in post Hassle Lines Strategy implementing.&#x0D; Keywords: Hassle Lines Strategy, Speaking Skills, Learning Strategy
Security Evaluation of Pailiar Homormophic Encryption Scheme

In modern days of information security, much attention is drifted towards achieving the major security triad of privacy, authentication and availability. Pailiar homormophic encryption is one of the most widely area of pubic key encryption schemes researchers are exploring to enhance information security. In this paper, we presented an overview of the Pailiar cryptosystem. We further evaluated the security vulnerabilities in the cryptosystem. This was achieved through mathematical theorems and inductions. This is to present some open issues for further research to propose and implement a more robust security system based on the Pailiar homormophic encryption scheme.
Ontology for Crop Management-A Core Vocabulary of Agricultural Activity

Ontology is an explicit specification of a conceptualization".This paper describes the construction of ontology for the agricultural domain, and the crop management in particular.Ontology together with a set of individual instances of classes constitutes a knowledge base.TNAUCrpOnt is the proposed ontology tool used for facilitating indexing and searching processes in a repository environment.This repository contains information in the form of instances.It contains various concepts related to the crop management.A user friendly Ontology is developed for farmers, scientists and students for accessing information with more accuracy and clarity.
Discrete maximum principle in systems with a delay in control

In the paper, we consider a discrete optimal control problem with a delay in control.Taking into account the specificity of this problem, we offer a new research method.The essence of this method is that unlike previously known ones here we assume that the set of admissible speeds satisfies convexity type conditions at the moment t + h, but not at t, where h is time delay in the system.The discrete analog of Pontryagin's maximum principle is obtained under weakened assumptions.An example showing the content-richness of the obtained result is given.