Reviewerly/exHarmony huggingface dataset:
Charleston Conference Proceedings, 2022

The Charleston Conference continues to be a major event for information and idea exchange among librarians, vendors, and publishers. Although 2022 was thankfully a less disruptive year than 2021, changes continued to be seen in the industry. Conference attendees continued to remark on the informative and thought‐provoking sessions in a hybrid environment. This is the eighteenth year that Beth R. Bernhardt, Consortia Account Manager at Oxford University Press, has put together the proceedings from the Conference. It’s the fourteenth year for Leah Hinds, Executive Director, and the sixth year that we are joined by Lars Meyer, Associate University Librarian, Access and Resource Services at Emory University. We are pleased to share some of the learning experiences that we, and other attendees, had at the conference.
Higher Order Semblance Based on Norm Ratio: A Case Study in Bohai Bay Basin

Summary Seismic coherence is a convenient and effective technique for characterizing the morphology of stratigraphic discontinuities which has been widely used in seismic exploration. The method based on semblance is widely practiced by researchers for its simple and effective. As an energy ratio attribute, it can be also thought as the squared correlation of windowed seismic data with a constant. In this paper, we will demonstrate a class of new semblances that are higher order semblances which are actually the ratios of different norms. As an extension of traditional semblance, they have a better characterization of faults which is due to the extension of data frequency that the weak discontinuities come to be enhanced. According to higher order semblances, we realize an accurate cognition of the fracture mod from a local region in Bohai Bay Basin.
Samawa Part of Speech Tagging using Brill Tagger

There exist 7,097 living languages in the world cited by Ethnologue. Most of them, however, do not exist on the Internet as the objects of research. It indicates the gap in language resources. One of them is Samawa language which has over 500,000 native speakers and is identified as endangered language by UNESCO. What we known about Samawa so far is a lack of information, tools, and resources to maintain its sustainability. This paper aims to contribute to NLP, a growing field of research, by exploring Samawa part of speech tagging problem using rule-based approach, i.e. Brill tagger. It has been trained on very limited data of Samawa corpus, which is 24,627 tokens including punctuation marks with 24 tags of our original tagset. K-fold cross-validation (k = 5 and k = 10) was applied to compare Brill’s performance with Unigram, HMM, and TnT. Brill tagger with the combination of default tagger, Unigram, Bigram and Trigram as baseline tagger achieve higher accuracy over 95% than others. It suggests that the Brill tagger can be used to extend Samawa corpus automatically.
Game Values of Strong Placement Games

The legal positions of a strong placement game, such as Domineering, form a simplicial complex called the legal complex. In this paper, we use the legal complex to study the game values taken on by the class of strong placement games using the legal complex. We show that many interesting values are possible, including all numbers and nimbers. We further consider how structures of the legal complex influence which values are possible.
Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending

We propose the first multi-frame video object detection framework trained to detect great apes. It is applicable to challenging camera trap footage in complex jungle environments and extends a traditional feature pyramid architecture by adding self-attention driven feature blending in both the spatial as well as the temporal domain. We demonstrate that this extension can detect distinctive species appearance and motion signatures despite significant partial occlusion. We evaluate the framework using 500 camera trap videos of great apes from the Pan African Programme containing 180K frames, which we manually annotated with accurate per-frame animal bounding boxes. These clips contain significant partial occlusions, challenging lighting, dynamic backgrounds, and natural camouflage effects. We show that our approach performs highly robustly and significantly outperforms frame-based detectors. We also perform detailed ablation studies and validation on the full ILSVRC 2015 VID data corpus to demonstrate wider applicability at adequate performance levels. We conclude that the framework is ready to assist human camera trap inspection efforts. We publish code, weights, and ground truth annotations with this paper.
Characterization of Mineralogy, Petrography, Geochemistry and Petrogensis of Basaltic Outcrops in Jurf Ed Darawish Area, Central Jordan

This research was conducted to investigate the mineralogy, petrography, geochemistry and petrogensis of the basaltic flows in Jurf Ed Darawish (JDB) area of central Jordan.Sexton representative basalt rock samples were selected from the studied JDB outcrops.Modally, JDB consists of plagioclase, olivine, pyroxene (diopside), opaque's, calcite and iddingsite minerals.Petrographically, basalt is holocrystalline, hypidiomorphic fine to medium grained and exhibited aphanitic to porphyritic texture.The common textures of the JDB rock samples were aphanitic, porphyritic, trachytic, glomeroporphyritic, sub ophitic, vesicular, and amygdaloidal.Geochemically, all of the inspected samples of JDB are located within Trachy basalt and plate alkaline basalt.The tectonic setting of JDB was plotted within the calcalkaline basalt and continental basaltic field.The rare-earth elements showed enrichment of the Ba and K, depletion of Ce relative to K, and enrichment of Nb and Pb with depletion of Y and positive Nb, Zr and Ti anomalies.Negative anomalies of Ba, Sr, Ti and P may be attributed to the fractionation of feldspar for Ba and Sr depletion apatite for P depletion.The positive Nb peak conforms to the tertiary as well as to recent continental alkali basalt provinces and acts as an indicator to the JDB product for the lithosphere from upwelling of the asthenosphere mantle.
Make an Intranet network to build a modern supermarkets chains in Kalar city to improve sales and performance via main controller and multi branches

The chain supermarket in the domestic have been grown up. Because of the proffesional staff, the quick development of enterprise, the information technology has been elevated to a new step. But, the lack of alignment between strategy and business goals is the source of the problems. So, business areas should effectively managing those projects before the IT area. Then, an efficient IT management is necessary, so that IT projects should be show up in relation with strategic alignment view and the achievement of organization goals. The aim of this work is to build an intranet network to connect a chain of supermarkets for the city by using the newest technology that simplify management information system. The work helps owners and managers gather information relating to their company. Meanwhile, right now in the city the supermerks manually working or at least the network is missing in the business field. Therefore, computerized management information systems is necessary to help much more convenient for manger to control and monitor all items in the branches. So, the supermarkets later on, much faster ,easier and powerful. Finally, it can helps to save time, money, and it makes the company become more accurate.
“The Third Teacher” of the XXI Century

Learning is a personal and an internal process influenced by different external factors. In the formal educational system learning is affected mainly by professors (first teacher), classmates (second teacher) and educational infrastructure (third teacher). The last element creates environments in which teaching and learning processes take place. The impact educational infrastructure has in academic achievements has been registered by many researchers (; ; ; ; and ). Nevertheless, it seems that Mexican Higher Educational Institutes (HEI) are being built the same way as in the past, therefore they don’t respond either to new educational paradigms, or the inclusion of emerging technology. This chapter refers to some problems “the third teacher” (i.e. the educational infrastructure) faces. Firstly, most schools and universities have been built to respond to specific needs of training, principally manual skills reacting to the Industrial Revolution (Robinson, 2011), but the postmodern society of XXI century requires different approaches (). Secondly, new information technology (IT) has changed the way people communicate and relate to each other. These include changes that affect directly the teaching and learning process and thus the educational infrastructure (Oblinger, 2005). Thirdly, designing Higher Educational infrastructure must be addressed differently than designing educational infrastructure for early schooling stages, as the students that enter universities are young adults and have different characteristics than children. Therefore, instead of grounding the design in pedagogy it must be an andragogy approach to tackling the problem.
September consultation #7

Dick, Burkhard H. MD, PhD, FEBOS-CR; Reinstein, Dan Z. MD, MA(Cantab), FRCSC, FRCOphth Author Information
A survey of cross-lingual features for zero-shot cross-lingual semantic parsing

The availability of corpora to train semantic parsers in English has lead to significant advances in the field. Unfortunately, for languages other than English, annotation is scarce and so are developed parsers. We then ask: could a parser trained in English be applied to language that it hasn't been trained on? To answer this question we explore zero-shot cross-lingual semantic parsing where we train an available coarse-to-fine semantic parser (Liu et al., 2018) using cross-lingual word embeddings and universal dependencies in English and test it on Italian, German and Dutch. Results on the Parallel Meaning Bank - a multilingual semantic graphbank, show that Universal Dependency features significantly boost performance when used in conjunction with other lexical features but modelling the UD structure directly when encoding the input does not.
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features

In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g., by their ability to recommend relevant and novel items to the target user. The user profile modelling stage, which is a key stage in content-driven RS is barely properly evaluated due to the lack of publicly available datasets that contain user preferences on content features of items. To raise awareness of this fact, we investigate differences between explicit user preferences and implicit user profiles. We create a dataset of explicit preferences towards content features of movies, which we release publicly. We then compare the collected explicit user feature preferences and implicit user profiles built via state-of-the-art user profiling models. Our results show a maximum average pairwise cosine similarity of 58.07\% between the explicit feature preferences and the implicit user profiles modelled by the best investigated profiling method and considering movies' genres only. For actors and directors, this maximum similarity is only 9.13\% and 17.24\%, respectively. This low similarity between explicit and implicit preference models encourages a more in-depth study to investigate and improve this important user profile modelling step, which will eventually translate into better recommendations.
USER EXPERIENCE OF A STYLUS PEN THAT CAN PROVIDE THE TEXTURE OF PEN AND PAPER

There is a lot of investment in research on displaying texture through a stylus pen. However, little is known about the effect of a stylus pen that can provide the texture of pen and paper on user experience. This study investigated whether there is a change in user experience when using a stylus pen that can provide pen-and- paper texture compared with a typical stylus pen. Experiments involving writing and drawing tasks using three tools (a stylus pen that can provide pen-and-paper texture, a typical stylus pen, and analogue pen and paper) were conducted, and user experiences were compared. After a literature review that examined the factors affecting the experience of using a pen, the experiments were conducted to measure usability and emotions. We found little difference between the typical stylus pen and the stylus pen in providing texture in respect of completion time and amount of error. However, the stylus pen that can provide texture showed results closer to those of the analogue pen and paper, and better results than those of the typical stylus pen in respect of familiarity and overall satisfaction of the usability and emotional variables. Therefore, this study confirmed that user experience is improved when using a stylus pen that can provide the texture of pen and paper.
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval

Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3D visualization or image analysis. In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs. However, GANs are known to have a difficulty with generating a diversity of patterns due to a phenomena known as mode collapse. To overcome the lack of generated pattern variety, we propose to condition the discriminator on the different body parts. Furthermore, our generator networks are extended to be three dimensional fully convolutional neural networks, allowing for the generation of high resolution images from arbitrary fields of view. In our verification tests, we show that the proposed method obtains the best scores by PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of the principle anatomy in high resolution. We expect that the proposed method contribute to effective utilization of the existing vast amounts of thick CT images stored in hospitals.
VeriSmart: A Highly Precise Safety Verifier for Ethereum Smart Contracts

We present VeriSmart, a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts. Writing safe smart contracts without unintended behavior is critically important because smart contracts are immutable and even a single flaw can cause huge financial damage. In particular, ensuring that arithmetic operations are safe is one of the most important and common security concerns of Ethereum smart contracts nowadays. In response, several safety analyzers have been proposed over the past few years, but state-of-the-art is still unsatisfactory; no existing tools achieve high precision and recall at the same time, inherently limited to producing annoying false alarms or missing critical bugs. By contrast, VeriSmart aims for an uncompromising analyzer that performs exhaustive verification without compromising precision or scalability, thereby greatly reducing the burden of manually checking undiscovered or incorrectly-reported issues. To achieve this goal, we present a new domain-specific algorithm for verifying smart contracts, which is able to automatically discover and leverage transaction invariants that are essential for precisely analyzing smart contracts. Evaluation with real-world smart contracts shows that VeriSmart can detect all arithmetic bugs with a negligible number of false alarms, far outperforming existing analyzers.
Lyrics Videos to Ease the Pronunciation Problem of Indonesian EFL Learner

Pronunciation is a basic for speaking skill. Rare in Senior High School, neither the teacher pay attention with the students’ pronunciation skill nor give them such media. This study was intended to investigate the effectiveness of lyrics video for teaching English pronunciation in Senior High School. This was quasi experimental study and the population was the second grade students. Reading aloud was given to the students as the pronunciation test. Their voice was recorded while they did the test. Since it was subjective test, so that inter-rater is used to avoid subjectivity in getting students’ score. The score was analyzed using T-test. In the end the result revealed that t-value (2.05) was higher than statistical value of t-table (1.676) at the significant level 5%. Therefore, the students who were taught using lyrics video of English song had better achievement in pronunciation rather than those who were not taught using lyrics video of English song.
THE EFFECTIVENESS OF USING INSTAGRAM IN DEVELOPING STUDENTS’ DESCRIPTIVE TEXT WRITING

ABSTRACTThis study investigates the effects of Instagram on students’ descriptive text writing of Islamic Senior High School students as well as to find out their perceptions of using Instagram for learning descriptive text writing. In doing so, the subjects consisted of 48 students who were in tenth grade. Then they were divided into two equal experimental and control groups (N=24).Their age ranged between 16 and 17 years old. Gender of participants was not considered as a variable in the study. This study used convergent parallel mixed-method whereas pre-test and post-test were administrated to collect quantitative data and questionnaire was given to collect qualitative data. The experimental group was taught descriptive text writing via Instagram application, whereas the control group followed Facebook application. Analyzing the data through the independent sample t-test revealed that with the help of Instagram application, the experimental group outperformed the control group in terms of writing descriptive text. Furthermore, Islamic Senior High School students had a positive attitude toward utilization of Instagram application.Keywords: Instagram, students, descriptive text writing
Minimization of Storage on Moving Objects in Video Surveillance

Investigation and so the number of surveillance cameras installed in public space is increasing. Many cameras installed at fixed positions are required to observe a wide and complex area, so observation of the video pictures by human becomes difficult. So there is a need for automation and dynamism in such surveillance systems. In order to allow the different users (operators and administrators) to monitor the system selecting different Quality of Service (QoS) are required depending on the system status and to access live and recorded video from different localizations i.e. from their mobile devices. More concretely, in Internet Protocol (IP) surveillance systems some resources involved are limited or expensive. So a technology using automatic detection of intruders (using image processing systems) and automatic alert systems will provide competitive advantage for surveillance systems. Video surveillance systems are the most important for crime investigation. Detection of suspicious human action is of great practical importance. Due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Our primary aim is to bring out a solution for the memory consumption during video recording and the problem of automatically tracking people and detecting unusual or suspicious movements in Closed Circuit TV (CCTV) videos. Our work presents a frameworkthat processes video data obtained from a CCTV camera fixed at a particular location. It is also used to reduce the storage location of recording videos as the videos will be recorded statically.I.
P6V2G: A Privacy-Preserving V2G Scheme for Two-Way Payments and Reputation.

The number of electric vehicles (EVs) is steadily growing. This provides a promising opportunity for balancing the smart grid of the future, because vehicle-to-grid (V2G) systems can utilize the batteries of plugged-in EVs as much needed distributed energy storage: In times of high production and low demand the excess energy in the grid is stored in the EVs’ batteries, while peaks in demand are mitigated by EVs feeding electricity back to the grid. But the data needed for managing individual V2G charging sessions as well as for billing and rewards is of a highly personal and therefore sensitive nature. This causes V2G systems to pose a significant threat to the privacy of their users. Existing cryptographic protocols for this scenario either do not offer adequate privacy protection or fail to provide key features necessary to obtain a practical system. Based on the recent cryptographic toll collection framework P4TC, this work introduces a privacy-preserving but efficient V2G payment and reward system called P6V2G. Our system facilitates two-way transactions in a semi online and post-payment setting. It provides double-spending detection, an integrated reputation system, contingency traceability and blacklisting features, and is portable between EVs. The aforementioned properties are holistically captured within an established cryptographic security framework. In contrast to existing protocols, this formal model of a V2G payment and reward system allows us to assert all properties through a comprehensive formal proof.
Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding

In this paper, we investigate a new approach to Population, Intervention and Outcome (PIO) element detection, a common task in Evidence Based Medicine (EBM).The purpose of this study is two-fold: to build a training dataset for PIO element detection with minimum redundancy and ambiguity and to investigate possible options in utilizing state of the art embedding methods for the task of PIO element detection.For the former purpose, we build a new and improved dataset by investigating the shortcomings of previously released datasets.For the latter purpose, we leverage the state of the art text embedding, Bidirectional Encoder Representations from Transformers (BERT), and build a multi-label classifier.We show that choosing a domain specific pre-trained embedding further optimizes the performance of the classifier.Furthermore, we show that the model could be enhanced by using ensemble methods and boosting techniques provided that features are adequately chosen.
Identification of Authenticity and Nominal Value of Indonesia Banknotes Using Fuzzy KNearest Neighbor Method

The existence of counterfeit money is often troubling the public. The solution given by the government to be careful of counterfeit money is by means of 3D (seen, touched and looked at). However, this step has not been perfectly able to distinguish real money and fake money. So there is a need for a system to help detect the authenticity of money. Therefore, in this study a system was designed that can detect the authenticity of rupiah and its nominal value. For data acquisition, this system uses detection boxes, ultraviolet lights and smartphone cameras. As for feature extraction, this system uses segmentation methods. The segmentation method based on the threshold value is used to obtain an invisible ink pattern which is a characteristic of real money along with the nominal value of the money. The feature is then used in the stage of detection of money authenticity using FKNN (Fuzzy K-Nearest Neighbor) method. From 24 test data, obtained an average accuracy of 96%. This shows that the system built can detect the authenticity and nominal value of the rupiah well.