Reviewerly/exHarmony huggingface dataset:
Explore the Transformation Space for Adversarial Images

Deep learning models are vulnerable to adversarial examples. Most of current adversarial attacks add pixel-wise perturbations restricted to some \(L^p\)-norm, and defense models are evaluated also on adversarial examples restricted inside \(L^p\)-norm balls. However, we wish to explore adversarial examples exist beyond \(L^p\)-norm balls and their implications for attacks and defenses. In this paper, we focus on adversarial images generated by transformations. We start with color transformation and propose two gradient-based attacks. Since \(L^p\)-norm is inappropriate for measuring image quality in the transformation space, we use the similarity between transformations and the Structural Similarity Index. Next, we explore a larger transformation space consisting of combinations of color and affine transformations. We evaluate our transformation attacks on three data sets --- CIFAR10, SVHN, and ImageNet --- and their corresponding models. Finally, we perform retraining defenses to evaluate the strength of our attacks. The results show that transformation attacks are powerful. They find high-quality adversarial images that have higher transferability and misclassification rates than C&W's \(L^p \) attacks, especially at high confidence levels. They are also significantly harder to defend against by retraining than C&W's \(L^p \) attacks. More importantly, exploring different attack spaces makes it more challenging to train a universally robust model.
Designing Mobile Learning App to Help High School Students to Learn Simple Harmonic Motion

The tremendous development of information and communication technology, especially the existence of smartphone, has a huge impact on people lifestyle. Smartphone technology allows several of applications to make life easier.   Most of teenagers in Indonesia have been familiar with smartphone technology which can be potentially used on educational field. In this study, we have developed mobile learning apps to help high school students in learning simple harmonic motion. In the past time, students who want to study physics by themselves usually only could rely on traditional books. With this mobile learning app, high school students can study anywhere and anytime. The developed mobile learning apps have been tested to a group of students consists of 34 high school students. We used one-group pre-test-and-post-test design to investigate the use of mobile learning app impacts on the students’ learning achievements. The average N-gain score of 0.33 shows that there is a medium gain of students’ learning achievements.
Certificateless Linkable Ring Signature Scheme

Ring signature is an anonymous signature that both authenticates the message and protects the identity information of the signer.It is suitable for scenarios such as anonymous network access, online auctions, etc.However, there is a problem in a regular ring signature scheme.The signer can generate multiple different signatures for the same message without being discovered by the verifier.This will of course cause some confusion.Linkable ring signature (LRS) resolves the problem.The verifier can determine if multiple signatures were generated by the same signer, but he cannot determine the actual signer's identity.In this paper, we first proposed the system model and security requirements for certificateless linked ring signature (CL-LRS).Then, we constructed a concrete CL-LRS scheme and gave the security proofs.Finally, we compared the efficiency of our scheme with several other LRS schemes.Our scheme does not use pairing operation and is more suitable for the computation-constrained environment.
Video spam comment features selection using machine learning techniques

&lt;p&gt;Nowadays, social media (e.g., YouTube and Facebook) provides connection and interaction between people by posting comments or videos. In fact, comments are a part of contents in a website that can attract spammer to spreading phishing, malware or advertising. Due to existing malicious users that can spread malware or phishing in the comments, this work proposes a technique used for video sharing spam comments feature detection. The first phase of the methodology used in this work is dataset collection. For this experiment, a dataset from UCI Machine Learning repository is used. In the next phase, the development of framework and experimentation. The dataset will be pre-processed using tokenization and lemmatization process. After that, the features to detect spam is selected and the experiments for classification were performed by using six classifiers which are Random Tree, Random Forest, Naïve Bayes, KStar, Decision Table, and Decision Stump. The result shows the highest accuracy is 90.57% and the lowest was 58.86%.&lt;/p&gt;
Tissue Structure Updating for In Situ Augmented Reality Navigation using Calibrated Ultrasound and Two-level Surface Warping

Objective: In minimally invasive surgery (MIS), in situ augmented reality (AR) navigation systems are usually implemented using a glasses-free 3D display to represent the preoperative tissue structure, and can provide intuitive see-through guidance information. However, due to changes in intraoperative tissue, the preoperative tissue structure is not able to exactly correspond to reality, which influences the precision of in situ AR navigation. To solve this problem, we propose a method to update the tissue structure for in situ AR navigation in such way to reflect changes in intraoperative tissue. Methods: The proposed method to update the tissue structure is based on the calibrated ultrasound and two-level surface warping technologies. Firstly, the particle filter-based calibration is implemented to perform ultrasound calibration and obtain intraoperative position of anatomical points. Secondly, intraoperative positions of anatomical points are inputted in the two-level surface warping method to update the preoperative tissue structure. Finally, the glasses-free real 3-D display of the updated tissue structure is finished, and is superimposed onto a patient by a translucent mirror for in situ AR navigation. Results: we validated the proposed method by simulating liver tissue intervention, and achieved the tissue updating accuracy of 92.86%. Furthermore, the targeting error of AR navigation based on the proposed method was also evaluated through minimally invasive liver surgery, and the acquired mean targeting error was 1.92 mm. Conclusion: The results demonstrate that the proposed AR navigation method is effective. Significance: The proposed method can facilitate MIS, as it provides accurate 3D navigation.
Segmentation-based image defogging using modified dark channel prior

Abstract Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques.
Privacy-Aware Secure Anonymous Communication Protocol in CPSS Cloud Computing

Cloud computing has emerged as a promising paradigm for the Internet of Things (IoT) and Cyber-Physical-Social Systems (CPSS).However, the problem of how to ensure the security of data transmission and data storage in CPSS is a key issue to address.We need to protect the confidentiality and privacy of users' data and users' identity during the transmission and storage process in CPSS.In order to avoid users' personal information leakage from IoT devices during the process of data processing and transmitting, we propose a certificateless encryption scheme, and conduct a security analysis under the assumption of Computational Diffie-Hellman(CDH) Problem.Furthermore, based on the proposed cryptography mechanism, we achieve a novel anonymous communication protocol to protect the identity privacy of communicating units in CPSS.In the new protocol, an anonymous communication link establishment method and an anonymous communication packet encapsulation format are proposed.The Diffie-Hellman key exchange algorithm is used to construct the anonymous keys distribution method in the new link establishment method.And in the new onion routing packet encapsulation format, the session data are firstly separated from the authentication data to decrease the number of cryptography operations.That is, by using the new onion routing packet we greatly reduces the encryption operations and promotes the forwarding efficiency of anonymous messages, implementing the privacy, security and efficiency in anonymous communication in cyber-physical-social systems.
Underwater Image Enhancement Based on Dehazing and Color Correction

Clear and clean underwater images can provide valuable information, which are crucial for developing, exploring, and protecting the underwater resources. However, the raw underwater image seldom fulfills the requirements concerning the underwater research due to the serious image degradation. To enhance single underwater image, we propose an effective and new method combined on the dehazing and color correction algorithm. First, we obtain the dehazed image by a fusion dehazing method calculated on the difference between maximum green-blue dark channels and maximum red dark channel, and the top 0:1% bright pixels in green-blue dark channels. Second, we obtain the enhanced image via the color restoration method corresponding to the human visual system. Finally, we use an efficient and simple weight fusion strategy to incorporate the dehazed image and the enhanced image for yielding the final high quality underwater image. Subjective assessment demonstrates that our method can remove haze, correct color cast, improve image brightness, and preserve image naturalness. The enhanced results obtain the highest values in terms of entropy, average gradient, UCIQE, UIQM, and CCF, which is superior to several existing underwater enhancement methods. Moreover, our method can improve other degraded image quality, such as low-light image and haze image, and can benefit the application tests, such as key points matching and edge detection.
A Novel Trust Computation Method Based on User Ratings to Improve the Recommendation

Today, the trust has turned into one of the most beneficial solutions to improve recommender systems, especially in the collaborative filtering method. However, trust statements suffer from a number of shortcomings, including the trust statements sparsity, users' inability to express explicit trust for other users in most of the existing applications, etc. Thus to overcome these problems, this work presents a method for computing implicit trust based on user ratings, in which four influential factors including Similarity, Confidence, Analogous Opinion, and Distance are utilized to achieve trust. For computing users’ similarity, the Pearson Correlation Coefficient measure was applied. Confidence was computed through users’ common in items rated. To compute users’ analogous opinions, what rating they have given to items was analyzed in three aspects of their satisfaction, dissatisfaction, and indifference about the items. Euclidean distance was employed on users’ ratings for computing the distance. Finally, the factors were combined to reach implicit trust. Moreover, fuzzy c-means clustering was applied to initially partition similar users for enhancing the performance positively. Finally, two MovieLens datasets of 100K and 1M have used to evaluate this approach, and results have shown that the approach significantly increases Accuracy, Precision, and Recall, compared to some other methods.
Knowledge guided diagnosis prediction via graph spatial-temporal network

Predicting the future health conditions of patients based on Electronic Health Records (EHR) is an important research topic. Due to the temporal nature of EHR data, the major challenge is how to properly model the sequences of patient visits. Recurrent Neural Networks (RNNs) with attention mechanisms are widely employed to address this challenge, but often vulnerable to data insufficiency. Lately, predictive models with the guidance of medical knowledge have been proposed to solve this problem and achieve superior performance. Although these models learn reasonable embeddings (infused with knowledge) for clinical variables, they are not able to fully make use of the underlying information in the knowledge graph. To address this, we propose an end-to-end robust solution, namely Graph Neural networks based Diagnosis Prediction (GNDP), to predict future conditions for patients. Compared with existing methods, GNDP learns the spatial and temporal patterns from patients' sequential graph, in which the domain knowledge is naturally infused. We evaluate our GNDP model against a set of state-of-the-art methods on two real-world EHR datasets and the results demonstrate that our approach significantly outperforms the baseline methods.
Emerging regulatory approaches to blockchain based token economy

Blockchain-enabled digital scarcity has opened up a whole new dimension of possibilities for the token economy, particularly with regard to rights and assets that have not been traded electronically before.Blockchain-based tokenization of rights and assets has also brought a new set of legal and regulatory challenges.Regulators and legislators are yet to address many of the issues raised by blockchain-based tokenization, from decentralisation and token characterisation to cross-border harmonisation and regulatory compliance with traditional market infrastructure.Lack of regulatory alignment can undermine many of the benefits of the token economy.Lack of legal certainty may not only stifle innovation and slow down mainstream adoption of blockchainbased tokenization, but can also raise the risks for investors and harm the reputation of the industry.The emerging regulations vary in approach.Liechtenstein became the first country to have a comprehensive technology-neutral regulation of the token economy.Malta and Singapore also represent progressive jurisdictions for blockchain regulation.However, most jurisdictions, including the United States and the European Union, have not yet formed a clear policy for blockchain regulation, and many legal questions remain open.The paper examines whether there is an emerging predominant regulatory approach or prevailing regulatory direction for the future of the token economy.It also highlights the existing regulatory void and divergent approaches to blockchain-based tokenization.Finally, the paper concludes that there is an urgent need to provide a clear legal and regulatory framework if the potential of the token economy is to be realised.
Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach

Cross-site account correlation correlates users who have multiple accounts but the same identity across online social networks (OSNs). Being able to identify cross-site users is important for a variety of applications in social networks, security, and electronic commerce, such as social link prediction and cross-domain recommendation. Because of either heterogeneous characteristics of platforms or some unobserved but intrinsic individual factors, the same individuals are likely to behave differently across OSNs, which accordingly causes many challenges for correlating accounts. Traditionally, account correlation is measured by analyzing user-generated content, such as writing style, rules of naming user accounts, or some existing metadata (e.g., account profile, account historical activities). Accounts can be correlated by de-anonymizing user behaviors, which is sometimes infeasible since such data are not often available. In this work, we propose a method, called ACCount eMbedding (ACCM), to go beyond text data and leverage semantics of network structures, a possibility that has not been well explored so far. ACCM aims to correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks. It models and understands latent representations of accounts using an embedding framework similar to sequences of words in natural language models. It also learns a transformation matrix to project node representations into a common dimensional space for comparison. With evaluations on both real-world and synthetic data sets, we empirically demonstrate that ACCM provides performance improvement compared with several state-of-the-art baselines in correlating user accounts between OSNs.
Random Self-reducibility of Ideal-SVP via Arakelov Random Walks

Fixing a number field, the space of all ideal lattices, up to isometry, is naturally an abelian group, called the Arakelov class group. This fact, well known to number theorists, has so far not been explicitly used in the literature on lattice-based cryptography. Remarkably, the Arakelov class group is a combination of two groups that have already led to significant cryptanalytic advances: the class group and the unit torus. In the present article, we show that the Arakelov class group has more to offer. We start with the development of a new versatile tool: we prove that, subject to the Riemann Hypothesis for Hecke L-functions, certain random walks on the Arakelov class group have a rapid mixing property. We then exploit this result to relate the average-case and the worst-case of the Shortest Vector Problem in ideal lattices. Our reduction appears particularly sharp: for Hermite-SVP in ideal lattices of certain cyclotomic number fields, it loses no more than a $$\tilde{O}(\sqrt{n})$$ factor on the Hermite approximation factor. Furthermore, we suggest that this rapid-mixing theorem should find other applications in cryptography and in algorithmic number theory.
Hybridizing extended ant lion optimizer with sine cosine algorithm approach for abrupt motion tracking

Abstract In view of the problem that the conventional tracker does not adapt to abrupt motion, we propose a tracking algorithm based on the hybrid extended ant lion optimizer with sine cosine algorithm (EALO-SCA) in this paper. Firstly, the multiple elites is used to replace the single elite in the standard ant lion optimizer (ALO). The extended ALO (EALO) can enhance the global exploration ability, which can handle abrupt motion. Secondly, considering that sine cosine algorithm (SCA) has strong local exploitation operator, a hybrid EALO-SCA tracker is proposed using the advantages of both EALO and SCA. The proposed approach can improve tracking accuracy and efficiency. Finally, extensive experimental results in both quantitative and qualitative measures prove that the proposed algorithm is very competitive compared to 7 state-of-the-art trackers, especially for abrupt motion tracking.
YouTube Channel: An Alternative Social Media to Enhance EFL Students’ Speaking Skill

The aim of this study is to know wether there is or not significant effect of using YouTube channel to students’ speaking skill. The use of YouTube channel can make easier for students to receive lessons, motivate students in learning, so what is the purpose of education can be achieved well. YouTube encourages positive learning attitude if it is incorporated in classroom teaching to supplement language learning especially speaking. The design of this study is experimental research that was conducted at English language study program of Universitas Islam Riau. The sample consist of 48 students from the first semester. After applying YouTube channel in learning, the results of the study showed a significant improvement. Based on the data of SPSS, the value of sig. (2-tailed) was 0,000 which is not greater than the probability value of 0.05. (0.000 &lt; 0.05). It can be concluded that Ho is rejected and H1 is accepted, means that there is significant effect of using YouTube channel towards students’ speaking skill.
A Semantic Based Approach for Topic Evaluation in Information Filtering

Topic Modelling has been successfully applied in many text mining applications such as natural language processing, information retrieval, information filtering, etc. In information filtering systems (IFs), user interest representation is the core part which determines the success of the system. Topics in a topic model generated from a user’s documents can be used to represent the user’s information interest. However, the quality of a topic model generated from a document collection is not always accurate because the topics of the topic model might contain meaningless or ambiguous words. This ambiguity problem can affect the performance of IFs which use a topic model to represent user information interest. Hence, a topic evaluation method to assess the quality of topics in a topic model is important for ensuring the effectiveness of utilizing the topic model in text mining applications. One method in measuring the quality of a topic model is to match the topical words of the model to concepts in an ontology. However, a limitation of this method is that some topical words in an examined topic cannot be found in the mapping ontology. In this study, we propose a new model to evaluate the quality of topics by matching concepts in an ontology. In particular, word embedding technique is applied to dealing with the ambiguity problem by finding similar concept words based on word embeddings. The assessed topics are then used in an information filtering system for filtering relevant documents for a user. The proposed model was evaluated against some state-of-the-art baseline models in terms of term-based, phrase-based, and topic-based user interest representations, and also some topic evaluation models. The result of the evaluation shows that the new proposed model outperforms the state-of-the-art baseline models