Reviewerly/exHarmony huggingface dataset:
Novel bit-serial semi-systolic array structure for simultaneously computing field multiplication and squaring

This paper presents a novel bit-serial semi-systolic array structure to simultaneously execute modular multiplication and squaring operations in GF(2 m ).The architecture is explored by using a systematic methodology based on the proper choice of the scheduling and projection vectors applied to the algorithm dependency graph.The explored architecture has the advantage of sharing the data-path between the two operations, and hence it leads to saving more space compared to the case of using a separate data-path for each operation.Also, the simultaneous calculation of both operations significantly decreases the execution time required to perform modular exponentiation operation, as it mainly depends on these two core operations.Complexity analysis indicates that the developed bit-serial semi-systolic array structure outperforms the latest exiting competitor bit-serial systolic and non-systolic structures in terms of area-time (AT) by at least 24%.This makes the proposed structure more appropriate for use in resource-constrained cryptographic processors.
PENGEMBANGAN MEDIA INTERAKTIF BERBASIS KOMPUTER PADA PEMBELAJARAN TEMATIK KELAS V SEKOLAH DASAR

The purpose of this study is to produce multimedia product that is useful for use in the learning process and can improve student learning outcomes. This study uses the Dick and Carey research and development methodology. The research instruments used in this study were interviews, questionnaires, and observations. The product feasibility is carried out by media experts, material experts and students. The results obtained from experts and students showed that the media developed showed feasibility as a learning medium.
Algoritma Naive Bayes Dalam Klasifikasi Lokasi Pembangunan Sumber Air

The purpose of this study can predict the feasibility of the location of the development of clean water sources in the Tirta Lihou PDAM using the Naive Bayes algorithm. With the increasing number of MBR (Low-Income Communities) that enter each year in each region, the Tirta Lihou PDAM plans to find alternative springs solutions for several production units so that they can meet the needs of the community. In determining the appropriate alternative sources of clean water in several production units, the datamining method is used. By using data mining techniques specifically classification using the Naive Bayes algorithm, predictions can be made on the feasibility of the location of the construction of clean water sources based on existing data. Naive bayes is a simple probabilistic prediction technique based on the Bayes theorem with a strong assumption of independence (dependence). Based on the results of calculations using algoritma naive bayes, the clasification results from 19 alternatives used, where there are 8 feasible classes and 11 classes are not feasible with the number of accuracy obtained at 78,95%. From the results obtained, it is expected that this research can help the PDAM Tirta Lihou in determining the location that is feasible to develop water sources so that it can meet the needs of the community. This research is also expected to be a reference for further researchers relating to the user algorithm used.
A Re-evaluation of Knowledge Graph Completion Methods

Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report the performance of several existing methods using our protocol. The reproducible code has been made publicly available
HRM PRACTICES AS DETERMINATES OF THE EMPLOYEE INTENTION TO STAY: THE MODERATING EFFECT OF TRANSFORMATIONAL LEADERSHIP: A CASE OF HIGHER EDUCATION INSTITUTES IN UAE

to stay among the lecturers working in the higher education institutes of UAE. In addition to that the study has examined the moderating role of transformational leadership in the relationship between HRM practices and intention to stay has examined. This study is important to the management and HR department in the education industry because through this study, higher learning education institutions are informed of the results of HRM practices and transformational leadership style which might have an influence on the lecturers’ intention to stay. The study is among the pioneering studies on the issues related to the HRM practices on the intention to stay. Methodology: So, current study has used SEM-PLS as statistical tool to answer the research questions raised in this study and research objectives envisaged in the current study. Results: The findings of the current study have provided support to with the hypothesized results. The study has argued that the to deal with the current external opportunities and menaces, new knowledge and skills for improving their existing and future performances must be provided for the organizations This study will be helpful for policymakers and researchers in examining the link between HRM practices on the intention to stay the higher education institutes of UAE.
Determination of In-Plane Shear Properties of Laminate with V-Notch Rail Shear Test and Digital Image Correlation

Abstract This article presents the results of the application of Digital Image Correlation (DIC) to measurements of in-plane shear modulus and strength of three different carbon fiber reinforced laminates. Three different approaches to shear strain calculations via DIC are evaluated and compared with standard strain gage measurements. Calculation of shear strain based on averaging DIC strain values of strain gages area in most cases yielded results closest to strain gages, while measurements based on single point strain measuring differed the most from strain gages. These results are attributed to shear strain distribution in the center area of the specimen. Thermoplastic matrix fabric reinforced composite had the lowest shear strength at 5% of shear strain, but the highest ultimate shear strength and strain at failure. Of thermosetting materials, laminate reinforced with unidirectional carbon fiber had shear modulus about 10% lower, than fabric reinforced laminate, but higher ultimate strength and strain at failure. This behavior is attributed to the presence of weaves in fabric reinforcing the laminate, causing shear stiffening of the material, but lowering its ability to deform under shear loading.
Generating Historical Maps from Online Maps

This paper proposes an automatic system to generate a large amount of data for the training of text detection systems for historical maps. The system takes online maps as input and learns a conditional GAN model, to generate realistic historical map images from existing geographic datasets. Then the system uses the generated images as the base map and inserts synthetic text. Since the system has the control of text content, font style, and location, the system can obtain ground truth information (minimum bounding boxes) of the synthetic text. To overcome the challenge of content mismatch, the proposed system uses a novel loss function to encourage the generation of historical cartographic symbols in the foreground areas and discourage the generation in the background. The final output is a set of images resembling historical maps and the minimum bounding boxes around text regions on the images as annotations.
A Label Informative Wide &amp; Deep Classifier for Patents and Papers

Muyao Niu, Jie Cai. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Blockchain-Based Ubiquitous Transport and Logistics Monitoring System

Logistics and transport have become an increasingly important topic in recent years. In this kind of scenario, being able to offer added value to users means an important improvement in the services offered by the different companies. Different actors take part in the process of sending, transportation and collection of the different products through roads, ports and airports. During this process different aspects of security must be taken into account, since an error in one of them can cause major problems for public health, nature or even the economy of a country. With all this in mind, this work proposes a new system that allows us, through the use of wireless technologies such as Radio Frequency Identification, Global Positioning System, Wi-Fi Direct and mobile communication technologies, to automate the process of authentication and tracking of different products and goods. The proposed scheme allows through the use of the cloud the creation of a ubiquitous communications system for monitoring, control and even automation of repositioning orders automatically. In addition, the use of a system based on blockchain allows to carry out the whole process in an optimal way that guarantees the privacy and immutability of the data referring to the transports and merchandise. This work allows to reduce costs and improve the quality of the processes of sending, transport and reception of products for the different companies involved in the supply chain.
A model-based method with geometric solutions for gaze correction in eye-tracking

The eyeball distortions caused by eye diseases, such as myopia and strabismus, can lead to the deviations of eye-tracking data. In this paper, a model-based method with geometric solutions is proposed for gaze correction. The deviations of estimated gaze points are geometrically analyzed based on the individual eyeball models with considerations of the distortions caused by myopia and strabismus. A set of integrated geometric solutions is derived from the varied situations including the case of strabismus and the case of myopia and strabismus, and then used for gaze correction in eyetracking. The experimental results demonstrate that this model-based method is effective to reduce deviations in estimated gaze points, and can be used to correct the modeling error in eye-tracking. Moreover, the proposed method has the potential to provide a simple approach to correct the eyetracking data for various populations with eye diseases.
A Survey on the Latest Development of Machine Learning in Genetic Algorithm and Particle Swarm Optimization

The concept of machine learning (ML) is becoming popular day by day among research community for many reasons—capability of solving high-dimensional real-world problem, availability of quality data to build and validate models and most importantly availability of required software supports. It has a great scope in several areas such as banking and financial services, government services, education, health care and transportation. The most important phases of any ML process are learning, also termed as training, and predicting. In order to match the outcome of the prediction with the available data, training of a ML model is required based on certain statistical and mathematical algorithms. In the next phase, accuracy of the predictions is judged based on certain statistical estimates and mathematical computations. Recently, interaction between ML concepts and various nature-inspired optimization techniques has received a great attention among researchers to solve real-world problems. The ML techniques are combined with nature-inspired optimization techniques to optimize the solution to solve any problem. In this chapter, work related to two well-known nature-inspired optimization algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, applied in machine learning are presented and reviewed. The combined studies on both the approaches help the researchers to understand and apply similar approaches to identify optimized solution to a problem in future.
Hybrid method for prediction of users’ information behavior in the Internet based on bioinspired search

Abstract The paper presents the method of predicting the learner preferences to make recommendations for building the behavior profile for recommender systems (assistants) personalizing the educational activity in the Internet space. To form the personal information offer and to identify the groups of collaborative filtration of the users with similar preferences, the authors propose the hybrid model of collaborative filtration based on the Item-Item CF and User-User CF methods simultaneously. To reduce the size of space during the search process, the author present a novel heuristic bioinspired algorithm based on the fish school behavior in nature. This method can be characterized by the ability to scale and process the sparse data. To estimate the quality of the developed algorithm, experimental research was carried out on the basis of the benchmark dataset MovieLens.
Computing Stable Demers Cartograms

Cartograms are popular for visualizing numerical data for map regions. Maintaining correct adjacencies is a primary quality criterion for cartograms. When there are multiple data values per region (over time or different datasets) shown as animated or juxtaposed cartograms, preserving the viewer’s mental map in terms of stability between cartograms is another important criterion. We present a method to compute stable Demers cartograms, where each region is shown as a square and similar data yield similar cartograms. We enforce orthogonal separation constraints with linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability). Our method guarantees ability to connect most lost adjacencies with minimal leaders. Experiments show our method yields good quality and stability.
Tagger for Polish Computer Mediated Communication Texts

In this paper we present a morphosyntactic tagger dedicated to Computermediated Communication texts in Polish.Its construction is based on an expanded RNN-based neural network adapted to the work on noisy texts.Among several techniques, the tagger utilises fastText embedding vectors, sequential character embedding vectors, and Brown clustering for the coarse-grained representation of sentence structures.In addition a set of manually written rules was proposed for postprocessing.The system was trained to disambiguate descriptions of words in relation to Parts of Speech tags together with the full morphological information in terms of values for the different grammatical categories.We present also evaluation of several model variants on the gold standard annotated CMC data, comparison to the state-of-the-art taggers for Polish and error analysis.The proposed tagger shows significantly better results in this domain and demonstrates the viability of adaptation.
Hacking VMAF with Video Color and Contrast Distortion

Video quality measurement takes an important role in many applications. Full-reference quality metrics which are usually used in video codecs comparisons are expected to reflect any changes in videos. In this article, we consider different color corrections of compressed videos which increase the values of full-reference metric VMAF and almost don’t decrease other widely-used metric SSIM. The proposed video contrast enhancement approach shows the metric in-applicability in some cases for video codecs comparisons, as it may be used for cheating in the comparisons via tuning to improve this metric values.
Knowledge Learning Symbiosis for Developing Risk Prediction Models from Regional EHR Repositories.

Secondary use of regional EHR data suffers several problems, including data selection bias and limited data size caused by data incompleteness. Here, we propose knowledge learning symbiosis (KLS) as a framework to incorporate domain knowledge to address the problems and make better secondary use of EHR data. Under the framework, we introduce three main categories of methods: knowledge injection to input features, objective functions, and output labels, where knowledge-enhanced neural network (KENN) was first introduced to inject knowledge into objective functions. A case study was conducted to build a cardiovascular disease risk prediction model on the type 2 diabetes patient cohort using regional EHR repositories. By incorporating a well-established knowledge risk model as domain knowledge under our KLS framework, we increased risk prediction performance both on small and biased data, where KENN showed the best performance among all methods.
Enhanced Learning of Jazz Chords with a Projector Based Piano Keyboard Augmentation

Learning jazz piano is considered technically difficult. Most people cannot afford private piano tuition and there are many freely available video tutorials on the Internet. This study identified a set of challenging topics associated with learning jazz piano based on popular YouTube jazz piano tutorials. The results suggest that learners are struggling to understand the construction of complex chords in the 12 keys. Most tutorial videos show two parallel keyboards, one physical with the real hands and one virtual keyboard, which the viewers must map onto their own keyboard. Based on these results a series of visualization approaches were explored through a low-fi prototype by augmenting the physical piano keyboard with voicing instructions using a projector.
Individual Educational Programs as New Mechanism of Integration between Higher Education and Labor Sphere

At present the problem of the integration between the higher education and the sphere of labor has become particularly urgent. It is noted that the attempts earlier made by a number of authors to solve the problem of the integration between the higher education and the labor sphere by combining educational and professional standards, as well as by harmonizing the requirements of the labor market for the qualiﬁcations of specialists and the competencies of graduates of higher education are facing signiﬁcant diﬃculties. In this paper, we consider individual educational programs as one of the mechanisms of combining the higher education and labor. The analysis of the emerging diﬀerences between the higher education and the sphere of labor in the context of diﬀerent educational models has been conducted. Thus, for example, within the framework of the professional model of the educational process organization, the possibilities of building individual educational programs are limited by the amount of labor intensity of the variable part of the educational program according to the ﬁeld of study or subject specialism. At the same time, we are talking about professional individual educational programs, which are based on the processes of the formation of professional subjectivity of the student and the formation of his/her professional image. Such individual educational programs not only orient students on a certain type of work activity already at university, but also guarantee a well-grounded choice of the future profession. The paper focuses on the signiﬁcant impact of the individualization of professional and professionalization of individual educational programs, as well as the multilevel structure of higher education on the nature of the integration of educational programs of higher education and the trajectories of professional development of young professionals. It is emphasized that the adaptation of students to the educational process, built on the principles of individualization of educational programs, is a key point of interdisciplinary psychological and pedagogical problems of the development of creative abilities of students and is accompanied by the mastery of new skills.
A Granularity-Based Intelligent Tutoring System for Zooarchaeology

This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt (Alicante, Spain). The coarse granularity performs a five-class prediction, the medium a twelve-class prediction, and the fine a fifteen-class prediction. In the coarse granularity, the results show that the first 10 most relevant attributes for classification are width, bone, thickness, length, bone fragment, anatomical group, long bone circumference, X, Y, and Z. Based on those results, a user-friendly interface of the tutor has been built in order to train archaeology students to classify new remains using the coarse granularity. A pilot has been performed in the 2019 excavation season in Abric del Pastor (Alicante, Spain), where the automatic tutoring system was used by students to classify 51 new remains. The pilot experience demonstrated the usefulness of the tutoring system both for students when facing their first classification activities and also for seniors since the tutoring system gives them valuable clues for helping in difficult classification problems.
Extracting Phenotypes of Cancer Patients from Electronic Health Records

In recent years, the rapid development of electronic health record (EHR) provides numerous opportunities for improving quality of healthcare. The EHR-based phenotyping is one of the important topics. It can facilitate many research and applications in the domain, such as diagnosis summary, novel phenotype discovery, identify patients for clinical trials and selecting cohort for clinical and generic research [1]. In general, a computable phenotype refers to a clinical condition or characteristic that can be ascertained by a computerized query to an EHR system or clinical data repository [2]. This ongoing project is aimed to extract the phenotypes of breast cancer patients to support the future research.
FUNDAMENTAL STUDY ON COLORED MORTAR USING CONCRETE PIGMENTS WITH DIFFERENT WATER BINDER RATIO

Colored concrete is concrete that is colored by adding pigment to the concrete mix. In recent years, such concrete has come into wide use for non-structural member like a curtain-wall and a slab. However, colored concrete technology has spread only for ordinary strength concrete. There are almost no studies regarding the application of a concrete with low water binder ratio like a high strength concrete.
Ciphertext Outdate Attacks on the Revocable Attribute-Based Encryption Scheme With Time Encodings

Cloud storage is a new computing paradigm that allows users to store their data in the cloud and access them anytime anywhere through the Internet.To address the various security issues that may arise in the cloud storage accessed by a large number of users, cryptographic encryption should be considered.Currently, researches on revocable attribute-based encryption (RABE) systems, which provide user revocation function and ciphertext update function by extending attribute-based encryption (ABE) systems that provide access control to ciphertexts, are actively being studied.Recently, Xu et al. proposed a new RABE scheme that combines ABE and identity-based encryption (IBE) schemes to efficiently handle ciphertext update and user revocation functionality.In this paper, we show that there is a serious security problem in Xu et al.'s RABE scheme such that a cloud server can obtain the plaintext information of stored ciphertexts by gathering invalidated credentials of revoked users.Additionally, we also show that the RABE scheme of Xu et al. can be secure in a weaker security model where the cloud server cannot obtain any invalidated credentials of revoked users.