Reviewerly/exHarmony huggingface dataset:
A 3d Face Modeling Approach for in-The-Wild Facial Expression Recognition on Image Datasets

This paper explores the benefits of 3D face modeling for in-the-wild facial expression recognition (FER). Since there is limited in-the-wild 3D FER dataset, we first construct 3D facial data from available 2D dataset using recent advances in 3D face reconstruction. The 3D facial geometry representation is then extracted by deep learning technique. In addition, we also take advantage of manipulating the 3D face, such as using 2D projected images of 3D face as additional input for FER. These features are then fused with that of 2D FER typical network. By doing so, despite using common approaches, we achieve a competent recognition accuracy on Real-World Affective Faces (RAF) database and Static Facial Expressions in the Wild (SFEW 2.0) compared with the state-of-the-art reports. To the best of our knowledge, this is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.
Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks

Reduction of the number of parameters is one of the most important goals in Deep Learning.In this article we propose an adaptation of Doubly Stochastic Variational Inference for Automatic Relevance Determination (DSVI-ARD) for neural networks compression.We find this method to be especially useful in language modeling tasks, where large number of parameters in the input and output layers is often excessive.We also show that DSVI-ARD can be applied together with encoder-decoder weight tying allowing to achieve even better sparsity and performance.Our experiments demonstrate that more than 90% of the weights in both encoder and decoder layers can be removed with a minimal quality loss.
Solving Systems of Equations in Supernilpotent Algebras.

Recently, M. Kompatscher proved that for each finite supernilpotent algebra $\mathbf{A}$ in a congruence modular variety, there is a polynomial time algorithm to solve polynomial equations over this algebra. Let $\mu$ be the maximal arity of the fundamental operations of $\mathbf{A}$, and let \[ d := |A|^{\log_2 (\mu) + \log_2 (|A|) + 1}.\] Applying a method that G. Karolyi and C. Szabo had used to solve equations over finite nilpotent rings, we show that for $\mathbf{A}$, there is $c \in \mathbb{N}$ such that a solution of every system of $s$ equations in $n$ variables can be found by testing at most $c n^{sd}$ (instead of all $|A|^n$ possible) assignments to the variables. This also yields new information on some circuit satisfiability problems.
Reinforcement Learning in Multi-agent Games: Open AI Gym Diplomacy Environment

Reinforcement learning has been successfully applied to adversarial games, exhibiting its potential. However, most real-life scenarios also involve cooperation, in addition to competition. Using reinforcement learning in multi-agent cooperative games is, however, still mostly unexplored. In this paper, a reinforcement learning environment for the Diplomacy board game is presented, using the standard interface adopted by OpenAI Gym environments. Our main purpose is to enable straightforward comparison and reuse of existing reinforcement learning implementations when applied to cooperative games. As a proof-of-concept, we show preliminary results of reinforcement learning agents exploiting this environment.
Assessing Data Cybersecurity Using ISO/IEC 25012

The importance of data is ever-growing, and it is widely considered to be the most valuable asset of a company. Since data is becoming the main driver of business value, data security is a paramount concern for companies. In recent years, several standards related to security have emerged, most notably those of the ISO/IEC 27000 series. However, they are focused on management systems and security infrastructure, neglecting the security of the data itself. Other standards related to data quality, such as ISO 8000, also fail to address data security in depth. To this end, we propose in this paper a framework for the evaluation of data cybersecurity, consisting of a quality model (based on ISO/IEC 25012), an evaluation process (based on ISO/IEC 25040), and a tool for the visualization of the assessment results. This evaluation framework has been taken as the basis for a data cybersecurity certification scheme, which complements other certifiable standards related to data and security such as ISO/IEC 27001 and ISO 8000.
International book donors and public libraries as partners in primary school literacy development in Kenya

Purpose The purpose of this paper is to explore the impact of international library materials aid in primary schools and to outline obstacles to effective utilization for maximum literacy benefits among primary school children. Design/methodology/approach Data were gathered via interviews, observation, focus group discussions and document analyses. Findings Findings indicate that teachers were trained by Kenya National Library Services Kisii Branch staff on basics of library materials management before literacy materials were sent to the schools; teachers and pupils reported that development of vocabulary and better essay writing are some of the benefits of the donated materials; and culturally distanced materials and school dynamics impact negatively on the effective utilization of the donated library resources. Practical implications The authors recommend that donors work hand in hand with the Ministry of Education and other local stakeholders that it may be possible to address obstacles to proper and highly effective implementation of literacy empowerment projects. Originality/value The findings of this study are from original research and the implications must be treated as such.
Ore content estimation based on spatial geological data through 3D convolutional neural networks

Summary Current tools for identifying new exploration targets for gold are built for geologists to manually interpret data acquired from different sources. Scaling this approach to larger projects is not a trivial task. One possibility to tackle this problem is to use data-driven predictive modeling to discover relationships in the data which can then be applied throughout a mine to more readily identify exploration targets. Here, we propose a methodology based on machine learning that takes as input data points in space describing measured geological information in a mine, correlates this with the level of gold mineralization in known places through a 3D convolutional neural network, and uses the obtained model to estimate the level of gold mineralization in every region of the mine that has available geological information. We compare the obtained model with a baseline model and show that it outperforms the baseline in all the metrics used, providing a much more accurate estimate of presence of economic gold for geologists in their investigations.
Regularization of Extreme Learning Machines with information of spatial relations of the projected data

The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.
WEBSITE USAGE IN DIGITAL PUBLIC RELATIONS – AN ANALYSIS OF IT COMPANIES IN TURKEY

Digital public relations, which can be defined as the use of Internet-enabled tools such as social media management, social bookmarking and sharing, search engine optimization (SEO), creating and distributing online press releases, blogs, forums, news sites, corporate web pages, etc. are an indispensable field for companies and brands. In this research, it is aimed to analyze the content of corporate web sites used as a digital public relations tool. In this study, we take "Turkey’s Top 500 IT Companies" survey and analysed the content of newly established companies. Our study reveals that new all IT companies have a website. In technical side, they provide most of the criterias but they are unsufficient in content side. In total, almost half of the companies are under 50% coverage rate. When all of the categories were calculated, 51.8% of the criteria were met and 9 companies (53%) received a score above this rate.
Applying Sliding Mode Control to Massage Robot Apply for Healthcare Therapy

Today in epoch modern life, everyone needs some time to relax and take a break. There are many to relax so a good way to do it is through massage. So many physicians are needed these days. In order to promote the experience and skills of experts and reduce labor during the massage, a massage robot which can be replaced by the expert techniques with individualized manipulation parameter and strategy control is present in this paper. The massage therapy trajectory of end-effort is determined by using image processing which is described and information from image data also extracted to control massage robot. Since the robot is non-linear system. We can use many algorithms for control robot as PID, Fuzzy, PD-Fuzzy, etc. However, parameters of PID and Fuzzy controller can be chosen due to expert's experiences. A Sling Mode Controller (SMC) is applied to control massage robot for set point.
Deontic Dynamic Logic : a Retrospective

In this paper a retrospective is given on the development of deontic dynamic logic. It first reviews the basic system PDeL as introduced in 1988, with emphasis on conceptual issues and technical choices and properties. It then continues with later developments and applications by ourselves and related work by others. Thus we will see how contrary-to-duties and free choice permissions are treated, and how violations can be handled more expressively, including a way of dealing with red/green states and transitions.
Conceptual design and analysis of a dredger robot for irrigation canals

Purpose This paper aims to present a solution to dredging the irrigation canals using a robotic system. Considering the importance of irrigating water, the waste within the water canals should be avoided. Irrigation canals are artificial linear structures in the landscape that are used for transporting the water. One important problem in water transferring is the waste materials flow inside the water, and in some areas, they block the main stream, reducing the effective capacity of the canal. Among the waste materials, aquatic plants are grown on the surface of the canal that needs to be removed from the canal. This removal operation is conducted using chemical, biological, ecological and physical methods with complex supply systems. In addition, robotic systems are used as such complex systems. So, a robotic system is proposed to dredging the irrigation canals. The assumed robot was manufactured in AGRINS laboratory of Tehran University. Design/methodology/approach Design procedure, dynamic modelling and simulation of this robotic system are studied. To validate the system design before its construction, ADAMS software is used to perform simulations. Finally, performance evaluation of the dredger robot in the canal is studied based on the experimental data. Findings Results show that the design procedure has been correctly fitted to the real condition. Therefore, the designed robot could be easily used to dredging irrigation canals. Practical implications The assumed robot was manufactured in AGRINS laboratory of Tehran University. Originality/value Performing a dredging operation in the canals could be conducted by a new technique considering both free sides of the canal. Therefore, in this paper, a conceptual design of a 3-wheels stair dredger robot is numerically and experimentally studied.
Task-Technology Fit (TTF), User Satisfaction, and Information System (IS) Acceptance in Relief &amp; Social Services Sector

This research focuses on studying the management information system (MIS) used in 16 area offices of Relief and Social Services Program (RSSP) at UNRWA - Gaza Strip. The research followed a quantitative methodology and a deductive approach. A survey was administered and 217 responses were received out of 274 questionnaires distributed to the targeted full-time employees working in 16 area offices, representing a response rate of 79.20%. For data analysis, SmartPLS 2.0 was utilised. The study findings reveal a strong impact of task-technology fit on perceived usefulness, perceived ease of use, and user satisfaction. Moreover, it is concluded that task characteristics has a significant negative relationship with task-technology fit. In addition, technology characteristics and computer self-efficacy have a significant positive relationship with task-technology fit, while technology characteristics has the strongest effect on the task-technology fit. Theoretically, the study findings supported the merging of TAM and TTF models to study MIS utilisation.
A Two-Tiered Database Design Based on Core Components Methodology

The number of Industry 4.0, Internet of Things, and cloud service implementations are growing rapidly. In the resulting, emerging, cross-industry, cooperative environments, a common understanding of message standards will be necessary to enable better semantic interoperability among both traditional enterprise applications and business-to-business applications. In this paper, we first discuss issues with the current state of interoperability, which is based on industry sector-based message standards. We then propose a new database design, which incorporates the Core Components Technical Specification (CCTS) methodology, to resolve those issues. Finally, we analyze benefits that come with the new database design and identify new challenges that should be considered through future research.
Performance analysis of error control coding and diversity in image transmission on wireless channels

Abstract This paper discusses the performance of using error control coding that combines with diversity techniques in the transmission of images on wireless channels to get reliable communication on noise and fading. The performance of the transmission system is not only determined by the effectiveness of channel usage, but it is also determined by the reliability of the communication system that causes noise and fading. Error control coding used in this research is Reed Solomon (RS) code, while the diversity technique used in this research is Maximum ratio combining. RS code is used to correct errors that occur during wireless transmission, while Use of diversity Maximum Ratio techniques Combining expected can affect the number of errors so that the signal transmitted to the wireless channels becomes more robust against interference caused by transmission channels. By using error control coding and diversity, the quality of image transmission at the receiver is increased.
Earthquake Monitoring Network of Erzurum: ATANET

The main aim of this study is to introduce
Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras

In integrated surveillance systems based on visual cameras, the mitigation of adverse weather conditions is an active research topic. Within this field, rain removal algorithms have been developed that artificially remove rain streaks from images or video. In order to deploy such rain removal algorithms in a surveillance setting, one must detect if rain is present in the scene. In this paper, we design a system for the detection of rainfall by the use of surveillance cameras. We reimplement the former state-of-the-art method for rain detection and compare it against a modern CNN-based method by utilizing 3D convolutions. The two methods are evaluated on our new AAU Visual Rain Dataset (VIRADA) that consists of 215 hours of general-purpose surveillance video from two traffic crossings. The results show that the proposed 3D CNN outperforms the previous state-of-the-art method by a large margin on all metrics, for both of the traffic crossings. Finally, it is shown that the choice of region-of-interest has a large influence on performance when trying to generalize the investigated methods. The AAU VIRADA dataset and our implementation of the two rain detection algorithms are publicly available at this https URL.
Evaluation Benchmarks and Learning Criteriafor Discourse-Aware Sentence Representations.

Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether sentence representations include broader context information. We also propose a variety of training objectives that makes use of natural annotations from Wikipedia to build sentence encoders capable of modeling discourse. We benchmark sentence encoders pretrained with our proposed training objectives, as well as other popular pretrained sentence encoders on DiscoEval and other sentence evaluation tasks. Empirically, we show that these training objectives help to encode different aspects of information in document structures. Moreover, BERT and ELMo demonstrate strong performances over DiscoEval with individual hidden layers showing different characteristics.
ON WORK-RELATED OBJECTS OF PATENT LAW

On Work -Re lat ed Obje cts of Pate nt La w 311 objects of patent law has come a long way, which ended with the transition from the privilege and creation of inventions on a planned basis to the adoption of Part Four of the Civil Code of the Russian Federation.The content of the norms on patent law of that complies with the international provisions on the protection of intellectual propert y.
Department-specific collection assessment

Purpose The purpose of this paper is to introduce the pilot project of collection assessment performed by an academic library for the Computer Information Science (CIS) Department specifically and share the experience involved in this assessment. Design/methodology/approach In this descriptive research, the library gathered and analyzed a set of evaluation reports for CIS-related print and electronic materials. This assessment includes analyses of physical items, periodicals, electronic resources, peers’ databases, faculty syllabi, as well as departmental allocation fund. Findings This project shows the importance of collaboration between the library and academic departments on collection evaluation and assessment. It demonstrates that the assessments of a department-specific collection can provide the library with insights into the quality of the collection and help librarians better understand the information requirements of the faculty and students in the department. By analyzing the data collected in the assessment, the library can spend the tight budget on the most needed core materials related to the curriculum and research of the department. This project provides a good example for future routine assessments. Practical implications The procedure, results and future work of this assessment provide other academic libraries with insights into and practical solutions to department-specific collection assessments. Originality/value The study describes a set of collection assessment activities specific to a disciplinary department undertaken by Spiva Library. Few studies have been conducted to study department-specific collection assessments. This type of study can assist a university library to develop a core collection in support of teaching, learning and research for individual academic departments.
Optimizing for Measure of Performance in Max-Margin Parsing

Many learning tasks in the field of natural language processing including sequence tagging, sequence segmentation, and syntactic parsing have been successfully approached by means of structured prediction methods. An appealing property of the corresponding training algorithms is their ability to integrate the loss function of interest into the optimization process improving the final results according to the chosen measure of performance. Here, we focus on the task of constituency parsing and show how to optimize the model for the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score in the max-margin framework of a structural support vector machine (SVM). For reasons of computational efficiency, it is a common approach to binarize the corresponding grammar before training. Unfortunately, this introduces a bias during the training procedure as the corresponding loss function is evaluated on the binary representation, while the resulting performance is measured on the original unbinarized trees. Here, we address this problem by extending the inference procedure presented by Bauer et al. Specifically, we propose an algorithmic modification that allows evaluating the loss on the unbinarized trees. The new approach properly models the loss function of interest resulting in better prediction accuracy and still benefits from the computational efficiency due to binarized representation. The presented idea can be easily transferred to other structured loss functions.
Why Estimation Method of Recurrence Time Transition Probabilities with Regard to Genetic Algorithms Without Bit Mutation

Respecting genetic algorithms without bit mutation, our study is to submit unprecedented algorithm to procure tentative and notional results respecting recurrence time transition probabilities estimation for transient states.
Observational Campaign of the FluTuES project - Torre Meaípe

The main objective of the FluTuES (Turbulent Flows in Espírito Santo) project is to investigate the interaction of the atmosphere with the surface through the observational determination of the main components of the energy balance on oceanic and coastal surfaces in the state of Espírito Santo.The first region of study is being in Meaípe, Guarapari, where there is the presence of monazite sand.This work will briefly describe the installation, equipment and automated publication of the data obtained in the project on the site of the Air-Sea Interaction Laboratory (LIAM).
Applying Cross Project Defect Prediction Approaches to Cross-Company Effort Estimation

BACKGROUND: Prediction systems in software engineering often suffer from the shortage of suitable data within a project. A promising solution is transfer learning that utilizes data from outside the project. Many transfer learning approaches have been proposed for defect prediction known as cross-project defect prediction (CPDP). In contrast, a few approaches have been proposed for software effort estimation known as cross-company software effort estimation (CCSEE). Both CCSEE and CPDP are engaged in a similar problem, and a few CPDP approaches are applicable as CCSEE in actual. It is thus beneficial for improving CCSEE performance to examine how well CPDP approaches can perform as CCSEE approaches. AIMS: To explore how well CPDP approaches work as CCSEE approaches. METHOD: An empirical experiment was conducted for evaluating the performance of CPDP approaches in CCSEE. We examined 7 CPDP approaches which were selected due to the easiness of application. Those approaches were applied to 8 data sets, each of which consists of a few subsets from different domains. The estimation results were evaluated with a common performance measure called SA. RESULTS: there were several CPDP approaches which could improve the estimation accuracy though the degree of improvement was not large. CONCLUSIONS: A straight forward application of selected CPDP approaches did not bring a clear effect. CCSEE may need specific transfer learning approaches for more improvement.