Reviewerly/exHarmony huggingface dataset:
Teaching Game AI as an Undergraduate Course in Computational Media

We need to teach AI to students in and outside of traditional computer science degree programs, including those designer-engineer hybrid students who will design and implement games or engage in technical games research later. The need to rethink AI curriculum is pressing in a design education context because AI powers many emerging practical techniques such as drama management, procedural content generation, player modeling, and machine playtesting. In this paper, we describe a 5-year experimental effort to teach a Game AI course structured around a broad and expanding set of roles AI can play in game design (e.g., Adversary and Actor, as well as Design Assistant and Storyteller). This course sets up computer science and computer game design students to transform practices in the game industry as well as create new forms of media that were previously unreachable. Our students gained mastery over the relevant techniques and further demonstrated (via novel prototype systems) many new roles for AI along the way.
Video Images Enhanced by using Sigmoid-Logarithm Transform

Abstract It is challenging to enhance contrast in video images, because of the irregularity of contrast and lightness in each frame, and the long processing time to improve. In this research, we suggested a new method relied on sigmoid logarithm transform (SLT). In the video image, the SLT used to enhance lighting component in the HSV color space. To know the ability of enhancement the proposed method was compared with other methods as histogram equalization (HE), Multi Scale Retenix with Color Restoration (MSRCR) and non-traditional as Fusion at Weakly Illuminated Images (FWII), Depending on the average Natural Image Quality Evaluator (NIQE), Mean Squared Error for Hue (MSEH), Mean Squared Error for Saturation (MSES) for each video. By analyzing the results, it was found that the proposed algorithm has good results in the contrast and lightness enhancement.
E-commerce implementation in supporting business services strategy (case study at petshop gifaro evidence)

Abstract The aim of this paper is introduced the importance of information technology for all petshop business units. Givaro Petshop is a business to provides products for the needs of pet supplies and pet care services. Recently the Givaro Petshop still managed product inventory manually. To improve the business, Givaro Petshop need an e-commerce website that can improve the effectiveness of business processes.The development of the Givaro Petshop e-commerce website uses Systems Development Life Cycle (SDLC) with the Rapid Application Development Prototyping methodology. The results of this study are an e-commerce website that can be used to record inventory of products owned, record sales transactions, receive orders for animal care services, manage various reports needed and provide information to the general public.
Smartphone applications for young English language learners

Abstract The advancement of technology has permitted the widespread use of mobile applications in English language education. Hence, the term mobile-assisted language learning or MALL appeared in the literature since the early 21 st century. This paper highlights a study’s report on the smartphone applications’ pattern of learning English that is targeted for Muslim young English language learners (YELL). The authors decided to use systematic literature review approach to guide the mobile applications reviewing process. The availability of applications was first searched on Google Play using predetermined keywords that resulted in 250 mobile applications. Guided by the PRISMA model resulted in 26 smartphone applications that were finalised to be reviewed using the Analytical Framework of MALL. The findings demonstrated that smartphone applications are popular with vocabulary attainment that is crucial in the early stage of English language acquisition. Several interesting aspects may be explored further by including non-native Muslim YELL in MALL empirical findings, Islamic perspectives on the use of MALL for Muslim YELL’s English language development and framework development for reviewing MALL befitting Muslim learners.
Increasing the Security of RFID-based Classroom Attendance System with Shamir Secret Share

&lt;p&gt;This paper proposes an attendance system for increasing the security based on Shamir Secret Share algorithm. The use of RFID devices for classroom attendance is still vulnerable to certain attacks. Students usually make use of existing loopholes for prohibited things, such as forged attendance. Shamir Secret Share is a security method based on the Secure Multiparty Computation (SMC) concept. The SMC guarantees not only the confidentiality of external attacks but also of each member in the secure system. In the attendance scenario using Shamir Secret Share, a student and a lecturer must do tapping at the same time; otherwise, the secret that opens the lock for attendance at that class will not be opened. To realize this system, this paper uses two RFID modules, each of which is connected to one nodeMCU microcontroller. Both systems are connected to a database where the Shamir Shared Secret calculation is performed. Some experiment has been implemented for proving the concept. The result shows that some scenarios of fraud in RFID based attendance can be prevented.&lt;/p&gt;
The Impact of Specialized Corpora for Word Embeddings in Natural Langage Understanding.

Recent studies in the biomedical domain suggest that learning statistical word representations (static or contextualized word embeddings) on large corpora of specialized data improve the results on downstream natural language processing (NLP) tasks. In this paper, we explore the impact of the data source of word representations on a natural language understanding task. We compared embeddings learned with Fasttext (static embedding) and ELMo (contextualized embedding) representations, learned either on the general domain (Wikipedia) or on specialized data (electronic health records, EHR). The best results were obtained with ELMo representations learned on EHR data for the two sub-tasks (+7% and +4% of gain in F1-score). Moreover, ELMo representations were trained with only a fraction of the data used for Fasttext.
Using External Knowledge to Improve Zero-Shot Action Recognition in Egocentric Videos

Zero-shot learning is a very promising research topic. For a vision-based action recognition system, for instance, zero-shot learning allows to recognise actions never seen during the training phase. Previous works in zero-shot action recognition have exploited in several ways the visual appearance of input videos to infer actions. Here, we propose to add external knowledge to improve the performance of purely vision-based systems. Specifically, we have explored three different sources of knowledge in the form of text corpora. Our resulting system follows the literature and disentangles actions into verbs and objects. In particular, we independently train two vision-based detectors: (i) a verb detector and (ii) an active object detector. During inference, we combine the probability distributions generated from those detectors to obtain a probability distribution of actions. Finally, the vision-based estimation is further combined with an action prior extracted from text corpora (external knowledge). We evaluate our approach on the EGTEA Gaze+ dataset, an Egocentric Action Recognition dataset, demonstrating that the use of external knowledge improves the recognition of actions never seen by the detectors.
An improved support vector machine classifier based on artificial bee colony algorithm

Abstract Support vector machine (SVM) has unique advantages in the classification of small sample data. The selection of parameters has an important impact on the classification accuracy and generalization ability of SVM. Since the selection of parameters of SVM is usually based on experience, an improved SVM classification model based on artificial bee colony algorithm (GOABC-SVM) is proposed in this paper. In this model, firstly, the traditional artificial bee colony algorithm is optimized using the ideas of global optimal solution guidance and opposite learning. Secondly, we set the reciprocal of classification error rate as the fitness function, and use the improved artificial bee colony algorithm to obtain the optimal parameter combination of SVM. Experiments on a set of datasets of UCI show that the proposed model has higher classification accuracy and better generalization ability.
Sharing Same Elements in User Viewing History Data Securely Through Private Set Intersection Under User-centric Data Control

Recently, various over-the-top (OTT) streaming services as well as traditional broadcasts distribute numerous content every day, allowing users to watch their favorite content at any time. While users can choose from a quantity of content, they often view the same content mainly because most OTT streaming services implement recommendation systems. However, it is often difficult for users to realize the same content they viewed and enjoy sharing their opinions or feelings because they do not know each other when and which content they viewed. For the purpose of encouraging such enjoyable experiences, we propose a system architecture that allows users to share the same content they viewed by using the user's viewing history data. Such viewing history data is currently collected and stored by hundreds of different services and companies. Therefore, our system architecture adopts a user-centric data control model that allows users to collect and store their data on their own online storages, and use it for their purposes. If users are asked to disclose all of the raw data of their viewing history to each other or to third party when sharing, most of them will feel anxiety because the data often contains sensitive personal information. Therefore, we introduce a method using private set intersection (PSI), a cryptographic technique that allows users to share the same elements in the users’ viewing history data without revealing anything to each other except the elements at the intersection. We also demonstrate the feasibility of the architecture through use cases.
Segmentation of CAPTCHA Using Corner Detection and Clustering

Character segmentation is the key to CAPTCHA recognition. In order to solve the problem of low success rate of CAPTCHA segmentation caused by adhesive characters, an adhesion character segmentation algorithm based on corner detection and K-Means clustering was proposed. The algorithm performs corner detection on the CAPTCHA image of the adhesive characters, then uses K-Means clustering method to cluster the corner points of ROI, and determines the adhesion character segmentation line from the clustering results. The experimental results are compared with the drop-fall and skeletonization, in which the recognition accuracy of the image with serious adhesion is 92%. The result shows the superiority of the segmentation algorithm and provides a new method for the segmentation of adhesive characters.
Factors Influencing Behavioral Intention of Farmers to Use ICTs for Agricultural Risk Management in Malaysia

The primary aim of this research was to examine the factors influencing behavioral intention of farmers to use ICTs for agricultural risk management. The past research reveals that many researchers had tried to determine factors affecting behavioural intentions of the respondents and TPB has been applied as technology acceptance model in various contexts. However, predicting behavioral intentions to use ICTs for agricultural risk management has not been evaluated from the actual field. Therefore, the data were collected from 360 farmers through multistage cluster sampling technique. Multiple linear regression through SPSS was administered for statistical procedure. The findings confirmed that the theory (TPB) was statistically feasible to predict behavioral intention of farmers to use ICTs for agricultural risk management. The results further reveal that attitude was one of the most influenced of intention, which was followed by perceived behavioral control and lastly the subjective norms. Thus, using TPB in this context would be helpful for other researchers and academia to understand the influence of each construct in the model.
Learning spectrograms with convolutional spectral kernels.

We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions. We present a principled framework to interpret CSK, as well as other deep probabilistic models, using approximated Fourier transform, yielding a concise representation of input-frequency spectrogram. Observing through the lens of the spectrogram, we provide insight on the interpretability of deep models. We then infer the functional hyperparameters using scalable variational and MCMC methods. On small- and medium-sized spatiotemporal datasets, we demonstrate improved generalization of GP models when equipped with CSK, and their capability to extract non-stationary periodic patterns.
Keterampilan membaca puisi siswa sebuah modifikasi teknik membaca puisi Jose Rizal Manua

This study aims to describe students' reading skills using a modification of Jose Rizal Manua's declamator technique in his book Reading Poetry. This research was conducted on students of Al Azhar 22 Middle Islamic Primary School. The method used is a qualitative method using modeling. The data source from this study came from five students who came from different classes but had an interest in poetry reading activities. The data in this study consisted of videos, sound recordings, and student notes collected by the teacher. In this study, poetry reading techniques at three stages of work, namely the work of the brain, work of the heart, and work of the body. Can be concluded about the use of modifications to this technique can improve students' poetry reading skills. The average value of the calculated value = 82 is greater than the value of the minimum graduation criteria = 75. Can be perfected by using a modification of Jose Rizal Manua's poetry reading technique can improve student skills in reading poetry.
Provably secure counter mode with related-key-based internal re-keying

Block cipher cryptanalysis in related-key adversary model is usually underestimated, since it is believed that the conditions of this model could be hardly achieved in practice. Nevertheless, the use of keys with known relation between them (e.g. for constructing a lightweight key-derivation procedure) in the cryptographic schemes and protocols could allow to increase their efficiency without significant security loss. In this case base cryptographic primitives (e.g. block ciphers) must be secure in a strong enough related-key adversary model. We propose a new internally re-keyed block cipher mode of operation called CTRR (”CounTer with Related-key Re-keying mode”). We prove its security under the assumption that the underlying cipher is secure in the related-key adversary model. As far as the authors know, this mode is the first block cipher encryption mode whose proven cryptographic properties are essentially based on the related-key security of the underlying primitives. We also study the security of the Kuznyechik block cipher against the related-key attack. We have managed to propose the attack only on the essentially reduced (up to 4 rounds and with significantly simplified key schedule) variant of the Kuznyechik cipher. The attack requires approximately $$2^{12}$$ encryptions under $$2^{12}$$ related keys and $$2^{43}$$ offline encryptions to recover the secret key. We also discuss why extending such an approach to the original cipher seems to be impossible. We use these heuristic reasoning to make an assumption that the cipher is secure in the corresponding adversary model and can be used with the proposed mode. At the same time, of course, the related-key cryptanalysis of the Kuznyechik cipher has to be continued. The results were originally presented at the CTCrypt’2018 workshop.
LBPH-Based Face Recognition System for Attendance Management

Facial recognition is the mostly used biometric technique for identification and acknowledgment of people automatically. This technology is widely used in fields, such as surveillance systems, criminal identification, law enforcement and banking. On the other hand, facial recognition systems do not need any direct contact and acceptance of the person/user. Surveillance cameras at various locations have helped to identify crimes/criminals. Also, this technology can be further used for many user-friendly applications like finding the participations of people at various programs, attendance monitoring at workplaces, universities/colleges, to restrict unauthorized entries, etc. Traditional manual attendance marking as well as biometric fingerprint methods is a time-taking procedure for instructors as well as students in academic fields. This paper discusses about the usage of facial recognition technique to label the participation of the student automatically. To reduce the faults of other systems, face recognition system using LBPH is implemented.
The Size of the Crypto Economy: Calculating Market Shares of Cryptoassets, Exchanges and Mining Pools

Abstract Cryptoassets and related actors such as crypto exchanges and mining pools are now fully integrated into mainstream economic activity. A necessary corollary is that they have attracted heightened regulatory and investor scrutiny. Although some rules and obligations apply uniformly across all economic actors in a given sector, many others, such as antitrust laws and some financial regulations as well as investor decisions are informed by actors’ relative economic size—meaning that those with larger market shares can become more attractive regulatory or investing targets. It is therefore a foundational issue to properly measure the economic footprint of economic actors in the crypto economy, for otherwise regulatory oversight and investor decisions risk being misled. This has proven a remarkably difficult exercise for multiple reasons including unfamiliarity with the underlying technology and role of involved actors, lack of understanding of the applicable metrics’ economic significance, and the unreliability of self-reported statistics, partly enabled by lack of regulation. Acknowledging the centrality of cryptoasset size in a number of regulatory and policymaking areas and the fact that previous attempts have been incomplete, simplistic, or even plainly wrong, this paper presents the first systematic examination of the economic footprint of cryptoassets and their constituent actors—mining pools and crypto exchanges. We aim to achieve a number of objectives: to introduce, identify, and organize all relevant and meaningful metrics of crypto economic actors market share calculation; to develop associations between metrics, and to explain their meaning, application, and limitations so that it becomes obvious in which context metrics can be useful or not, and what the potential caveats are; and to present rich, curated, and vetted data to illustrate metrics and their use in measuring the shares of crypto economic actors in their respective markets. The result is a comprehensive guidance into the size of the crypto economy.
Non-Monotonic Sequential Text Generation.

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.
Robust Learning from Discriminative Feature Feedback

Recent work introduced the model of learning from discriminative feature feedback, in which a human annotator not only provides labels of instances, but also identifies discriminative features that highlight important differences between pairs of instances. It was shown that such feedback can be conducive to learning, and makes it possible to efficiently learn some concept classes that would otherwise be intractable. However, these results all relied upon perfect annotator feedback. In this paper, we introduce a more realistic, robust version of the framework, in which the annotator is allowed to make mistakes. We show how such errors can be handled algorithmically, in both an adversarial and a stochastic setting. In particular, we derive regret bounds in both settings that, as in the case of a perfect annotator, are independent of the number of features. We show that this result cannot be obtained by a naive reduction from the robust setting to the non-robust setting.
Dynamic Speech Trajectory Based Parameters for Low Resource Languages

The speech recognition problem deals with recognizing spoken words or utterances to interpret the voice message. This domain has been investigated by many researchers for more than five decades. There are numerous techniques and frameworks made available to address this problem. Hidden Markov Modeling (HMM) being a popular modeling technique has been used in different tools to build speech-based systems. In spite of its vast usage and popularity, shortcomings have introduced some new challenges in designing the feature modeling techniques. One of the solutions is using trajectory models. They are efficient in capturing the intra-segmental temporal dynamics which helps to understand the continuous nature of the speech signal. Even though trajectories have been found to be an effective solution, the complexity of trajectory modeling is yet to be improved. In this paper, two trajectory parameter extraction methods are proposed. The methods are shown to be effective for speech classification. The detailed procedures with results are discussed in this paper.
Culture-Based Explainable Human-Agent Deconfliction

Law codes and regulations help organise societies for centuries, and as AI systems gain more autonomy, we question how human-agent systems can operate as peers under the same norms, especially when resources are contended. We posit that agents must be accountable and explainable by referring to which rules justify their decisions. The need for explanations is associated with user acceptance and trust. This paper's contribution is twofold:i) we propose an argumentation-based human-agent architecture to map human regulations into aculture for artificial agents with explainable behaviour. Our architecture leans on the notion of argumentative dialogues and generates explanations from the history of such dialogues; andii) we validate our architecture with a user study in the context of human-agent path deconfliction. Our results show that explanations provide a significantly higher improvement in human performance when systems are more complex. Consequently, we argue that the criteria defining the need of explanations should also consider the complexity of a system. Qualitative findings show that when rules are more complex, explanations significantly reduce the perception of challenge for humans.