Reviewerly/exHarmony huggingface dataset:
Investigations of Site-Specific, Long Term Average Albedo Determination for Accurate Bifacial System Energy Modeling

In recent years, bifacial modules have been attracting industry interest due to the potential for significant contributions to energy generation from the module rear side. Albedo is a critical parameter needed for modeling rear side irradiation, and accurate albedo values representing both longterm and evolving conditions are needed to quantify the impact of the additional energy generation for financing solar projects. In this paper, the authors identify multiple sources of long-term albedo data and compare to shorter term ground-based measurements in Utah and California. An evaluation of groundbased albedo measurement techniques is presented. Finally, we evaluate methods to reduce higher-frequency measured or longterm albedo data to monthly values commonly needed by industry-accepted software, such as PVsyst.
Unsupervised Learning for Product Ontology from Textual Reviews on E-Commerce Sites

On modern e-commerce sites, textual reviews are rich in sentiment and opinions about a product, in particular, its specific attributes. Product ontologies, consisting of a taxonomic categorization of lists of such attributes and product types, are useful for a wide range of applications, such as opinion mining and sentiment analysis. Unfortunately, with a paucity of fine-grained hierarchical categorization of products, many smaller sites feature only coarse high-level categories. We present the Iterative Bootstrapping Process (IBP), an unsupervised learning method for such fine-grained hierarchical categorization of products using coarse categories from Chinese product textual reviews. Results show that our model can extract useful product attributes and still can achieve a high accuracy on the task for categorizing unseen products.
The “intraorogenic” Svecofennian Herräng mafic dyke swarm in east-central Sweden: age, geochemistry and tectonic significance

The Herräng mafic dykes form an E-W-trending dyke swarm within the Bergslagen lithotectonic unit of the Svecofennian orogen in east-central Sweden. They intrude the Svecofennian supracrustal rocks and early-orogenic granitoids, but are themselves cut by late Svecofennian pegmatites, and have undergone Svecofennian amphibolite-facies metamorphism, leading to their classification as “intraorogenic” Svecofennian dykes. They can be assigned an age between 1870 and 1850 Ma, with metamorphism of the dykes dated at 1848 ± 13 Ma by U-Pb in titanite. Their current mineralogy is dominated by metamorphic plagioclase and amphibole, with variable amounts of quartz and biotite, and minor to accessory titanite, apatite, epidote, pyrite, magnetite, ilmenite and zircon. Textures range from massive to strongly foliated. Twenty samples of dyke rocks from three subareas in the Roslagen region, including the Herräng type area, range in composition from basaltic to andesitic with 47 to 60 wt% SiO2, broadly similar to the Dannemora dykes and the Avesta-Östhammar gabbros and diorites. Initial 87Sr/86Sr ratios (at 1870 Ma) varies between 0.7026 and 0.7038, corresponding to initial εSr between +5 and +21, and initial εNd between −0.4 and +1.3, suggesting a slightly enriched to mildly depleted mantle source, similar to other Svecofennian mafic rocks. The dykes dominantly show a calc-alkaline volcanic arc signature related to subduction. They formed during an extensional episode, possibly related to incipient back-arc spreading or subduction roll-back following the main early-orogenic subduction-related Svecofennian magmatism, but penecontemporaneous with amphibolite-facies metamorphism in the area.
Document Similarity Detection Using Indonesian Language Word2vec Model

Most researches on text duplication in Bahasa uses the TF-IDF method. In this method, each word will have a different weight. The more frequencies the word appears, the greater the weight. This study aims to detect the similarity of documents by calculating cosine similarity from word vectors. The corpus was built from a collection of Indonesian Wikipedia articles. This study proposes two techniques to calculate the similarity which is simultaneous and partial comparison. Simultaneous comparison is direct comparison without dividing documents into several chapters, while partial comparison divides documents into several chapters before calculating the similarity. Similarity result from partial comparison is more accurate than simultaneous comparison. This study uses Unicheck application TF-IDF method as a benchmark. Similarity result from Unicheck and this study are different, due to the different method applied. Similarity result using TF -IDF method is smaller than using Word2vec, this is because TF-IDF can't detect paraphrase. The limitation in this study is that the Unicheck application used as a benchmark does not use the same method as the method used in this study other than that the determination of expected value is still subjective.
Research on the Extraction of Entity Relationships From Fusion Syntactic Information

Information extraction is an important branch in the field of natural language processing (NLP), and relationship extraction is particularly important as its basic task. At present, most approaches in this domain represent relationship extraction with a single word vector or combine different features to process relationship extraction. However, different methods have different advantages and disadvantages in the classification of different relationship types. Therefore, this paper proposes a method to separately use the syntactic structure information in syntactic information and the shortest dependency path as an input to the experiment. Then combined with the advantages of each method, the relationship types with outstanding classification results are extracted to increase the weight as an important basis for the subsequent relationship classification and the algorithm is optimized. Finally the relationship type is determined through the softmax activation function. At the same time, in order to capture the local key information of the sentence, the convolutional neural network (CNN) is incorporated to improve the feasibility of the experiment. This method not only extracts more sufficient physical relationships, but also enriches selectivity; the experimental results show that the F-score increases by 5.85%, which proves that this experimental method is feasible.
Leakage-resilient biometric-based remote user authentication with fuzzy extractors

Fuzzy extractors convert biometrics and other noisy data into a cryptographic key for security applications such as remote user authentication. Leakage attacks, such as side channel attacks, have been extensively modelled and studied in the literature. However, to the best of our knowledge, leakage attacks to biometric-based remote user authentication with fuzzy extractors have never been studied rigorously. In this paper, we propose a generic framework of leakage-resilient and privacy-preserving biometric-based remote user authentication that allows an authorized user to securely authenticate herself to a remote authentication server using her biometrics. In particular, the authorized user relies only on her secret biometrics to perform a valid authentication — which is suitable for user authentications in a cross-platform setting.
FP-Captcha: An Improved Captcha Design Scheme Based on Face Points

Captcha’s are random significant challenges, used to differentiate between humans and bots in order to prevent unauthorized access to web services. Over a decade lots of research have been carried out for generating numerous types of Captcha’s; specially using image and face detection and recognition techniques. Rather than machine, humans are outstandingly good at several things, including pattern recognition, linguistic abilities, and innovative thinking. Although, computers are rapidly improving the same, but most programs still don’t recognize as well as a child. Humans are inherently capable of detecting faces in a variety of contexts; even parts of face portions are not visible yet. Modern advancement in Artificial Intelligence, Deep Learning and advanced Image Processing techniques have made the Captcha based security system unsecure and vulnerable. However, as the time progresses Captcha’s are equipped with high usability, robustness and producing new unique challenges. This paper proposes a novel face point based Captcha, which employs various face points detection as its test, where user will ask to click on correct face points of all human faces presented in the Captcha challenge; which comprises of real and fake face images, with balanced noise and distortions, embedded in a composite background. Over 100 unique and random FP-Captcha’s are generated and solved by 115 UG-students, with accuracy of 97.39% for correct responses. Consequently, the probability of passing a FP-Captcha test by random guessing attack was 0.00000245%. Therefore, we conclude that, FP-Captcha is secure and robust against malicious attacks and offer better human accuracy.
WraPr

We propose a novel fabrication method for 3D objects based on the principle of spooling. By wrapping off-the-shelf materials such as thread, ribbon, tape or wire onto a core structure, new objects can be created and existing objects can be augmented with desired aesthetic and functional qualities. Our system, WraPr, enables gesture-based modelling and controlled thread deposition. We outline and explore the design space for this approach. Various examples are fabricated to demonstrate the possibility to attain a range of physical and functional properties. The simplicity of the proposed method opens the grounds for a light-weight fabrication approach for the generation of new structures and the customization of existing objects using soft materials.
Secure Outsourced Medical Data against Unexpected Leakage with Flexible Access Control in a Cloud Storage System

The application of cloud storage system has been deployed widely in recent years. A lot of electronic medical records (EMRs) are collected and uploaded to the cloud for scalable sharing among the authority users. It is necessary to guarantee the confidentiality of EMRs and the privacy of EMR owners. To achieve this target, we summarize a series of attack behaviors in the cloud storage system and present the security model against many types of unexpected privacy leakage. Privacy of unassailed EMRs is guaranteed in this model, and the influence of privacy leakage is controlled in a certain scope. We also propose a role-based access control scheme to achieve flexible access control on these private EMRs. One can access medical records only if his/her role satisfies the defined access policy, which implies a fine-grained access control. Theoretical and experimental analyses show the efficiency of our scheme in terms of computation and communication.
Analysis on the Criminal Subject of Artificial Intelligence

The development of artificial intelligence warns of possible social risk, and artificial intelligence crime put forward a new proposition for criminal law theory and legislation. From the perspective of criminal subject, artificial intelligence can be divided into artificial narrow intelligence (ANI) and artificial general intelligence (AGI). The ANI does not have independent judgment and decision ability, so it can hardly become the subject of criminal responsibility. However, the AGI has independent discrimination and control ability, and it can commit a crime by self-selection and identification, so it is more possible to become the criminal subject. At present, we should gradually construct and improve the scientific normative system of artificial intelligence crime on the basis of focusing on legislative foresight.
Evolutionary Behavioral Design of Non-Player Characters in a FPS Video Game Through Particle Swarm Optimization

Evolutionary computation covers the family of artificial intelligence techniques inspired by nature and biological evolution. These methods, such as swarm intelligence, may have a very positive impact on video games, for instance, for the design of Non-Player Characters (NPCs) to obtain a realistic intelligent behavior in a simple way. To this aim, we describe an evolutionary behavioral design of NPCs using particle swarm optimization in a first-person shooter video game. Several computer experiments have been carried out to analyze the feasibility and performance of this approach. Our experimental results show that the proposed method performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.
Segmentation of Balinese Script on Lontar Manuscripts using Projection Profile

Lontar manuscript is a Balinese cultural heritage that needs to be preserved so that it is not eroded by the progress of the times. The literary work contained in the lontar is a reflection of the life of Balinese people at that time. In the lontar, there are literary works that teach us a lot about life philosophy, religion, and many other knowledges that is very useful for us as a provision of life written using Balinese script. The preservation of Balinese culture, especially lontar manuscript is currently a special concern of the Bali Provincial Government. It takes a method of preservation of the cultural values contained in the lontar manuscript written in the Balinese script. This study aims to separate images containing Balinese script character with backgrounds. The object of Balinese script characters was separated by one character, so this study will produce Balinese script character images that have been separated from other characters. Segmentation can be used to separate the character of Balinese script with lontar manuscript. In this study applying the projection profile method in the segmentation process. The test was carried out using fifteen sheets of lontar manuscript in the segmentation process. The test results produce an accuracy value of 82.35% using the window value = 70 and the threshold value = 0.05.
Image Compression Algorithms Based on Super-Resolution Reconstruction Technology

In the era of intelligent media, we all need high quality image or video information. However, due to the limitation of bandwidth or storage resource, we usually use low bit rate coding technology for image compression. But the image distortion caused will bring in a visually inferior experience, and the actual information obtained is often not sufficient. Therefore, the super-resolution reconstructed image coding technology is researched to provide a higher dynamic bit rate variation range. Based on image super-resolution reconstruction, this paper proposes two targeted image compression coding frameworks. Firstly, we compress the DCT down-sampled image as the input of super-resolution reconstruction, and use image completion as the pretreatment process of image super-resolution reconstruction, which effectively improves the quality of reconstructed image at low bit rate and, hence improves the coding efficiency. Secondly, a new image compression technique is proposed here which is based on residual compensation and super-resolution reconstruction. In the encoder, the super-resolution reconstructed image is subtracted from the original image to obtain the residual image which is compressed. In the decoder, the residual image is decompressed and added to the reconstructed image to complete the decoding process which achieves flexible rate control.
Prototyping Interactive Fluidic Mechanisms

In this hands-on studio we introduce a method of designing and prototyping fluidic mechanisms that utilize the flow as both deformation sensors and displays. A fabrication process and the featured materials will be provided to allow participants to design and prototype self-contained fluidic channels. These channels are designed to respond to mechanical inputs such as deformation and pressure with flow and color change. We will introduce a specialized software plugin for design and flow simulation that enables simple and rapid modelling with optimization of the fluidic mechanism. The goal of this studio is to provide researchers, designers and makers with hand-on experience in designing fluidic mechanisms, coupling shape-change (i.e. deformation input) with displayed response. Our method allows participants to explore meaningful applications such as on-body wearable devices for augmenting motion and animating objects such as interactive books, lampshades and packaging.
A Formal System of Axiomatic Set Theory in Coq

Formal verification technology has been widely applied in the fields of mathematics and computer science. The formalization of fundamental mathematical theories is particularly essential. Axiomatic set theory is a foundational system of mathematics and has important applications in computer science. Most of the basic concepts and theories in computer science are described and demonstrated in terms of set theory. In this paper, we present a formal system of axiomatic set theory based on the Coq proof assistant. The axiomatic system used in the formal system refers to Morse-Kelley set theory which is a relatively complete and concise axiomatic set theory. In this formal system, we complete the formalization of the basic definitions of sets, functions, ordinal numbers, and cardinal numbers and prove the most commonly used theorems in Coq. Moreover, the non-negative integers are defined, and Peano's postulates are proved as theorems. According to the axiom of choice, we also present formal proofs of the Hausdorff maximal principle and Schröeder-Bernstein theorem. The whole formalization of the system includes eight axioms, one axiom schema, 62 definitions, and 148 corollaries or theorems. The “axiomatic set theory” formal system is free from the more apparent paradoxes, and a complete axiomatic system is constructed through it. It is designed to give a foundation for mathematics quickly and naturally. On the basis of the system, we can prove many famous mathematical theorems and quickly formalize the theories of topology, modern algebra, data structure, database, artificial intelligence, and so on. It will become an essential theoretical basis for mathematics, computer science, philosophy, and other disciplines.
Bio-CAPTCHA Voice-Based Authentication Technique for Better Security and Usability in Cloud Computing

Cloud computing has gained increased interest in the last few years, where an increasing number of providers are converging to such a promising platform. However, the security issues are still a big concern in the cloud, where authentication is a major one. Much research has been conducted to secure the authentication, where some of them used biometric features (fingerprint, face, and voice, etc.). In general, the biometric authentication techniques have a noticeable advantage compared to the traditional techniques because biometric features are hard to be altered or forged. Nevertheless, a new generation of attacks threatens the biometric security by using brute force approaches. This article proposes a nontraditional authentication technique that was called Bio-CAPTCHA. The proposed technique uses a random voice-based password challenge that dynamically changes every time the user tries to login, which promises to significantly decrease the possibility of unauthorized access. The conducted Experimental and theoretical analysis confirms the high-security level of the proposed technique.
Virtual Futures

In this chapter, the author explores how immersive media experiences might lend themselves to examinations of pathways to a preferred future. After surveying a number of immersive media projects—some that have dealt with environmental and social issues and some on broader topics—the author identifies a number of affordances of the form. These affordances are crafted into a ‘virtual futures manifesto', or a set of guidelines for the commissioning and creation of such works. The manifesto points include 1) stage an encounter; 2) be wild: bewilderment is powerful; 3) move from being to doing; 4) embody the future; and 5) care: the participants matter. It is hoped that immersive media experiences that consider these points may assist audiences in imagining pathways to preferred futures.
Snowflakes Removal for Single Image Based on Model Pruning and Generative Adversarial Network

The snow fluttering in the air seriously affects the quality of the image and destroys the completeness of the information in the picture. In this paper, we solve this problem by generative adversarial network training model and combining a method of model pruning. The neural network is used to remove snowflake. As the depth of the neural network increases, it is easy to appear over-fit, so we use model pruning optimization to reduce the computational volume of the deep learning model. In response to the above problems, we mainly deal with three steps: pruning the network; identify the snowflake area and use the area around the snowflake to recover the snowflake area; identify the restored picture to make sure that the restored image does not have much modification marks.
A Pufferfish privacy mechanism for monitoring web browsing behavior under temporal correlations

Monitoring web browsing behavior can benefit for many data mining tasks, such as top-k mining and suspicious behavior watching. However, directly releasing private browsing data to the public would raise user concerns from a privacy perspective. Differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in correlated data. For this reason, Pufferfish privacy, a recent generalization of differential privacy for correlated data, can be used. The goal of our work is to share useful statistics of on-line browsing behavior to perform monitoring tasks while protecting individual user privacy. To achieve this goal, the privacy requirements in our problem are specified in the Pufferfish framework firstly. Then a privacy leakage computation model (PLCM) is designed based on the previous privacy specification, which can be used to make a quantitative analysis of the maximum privacy leakage caused by temporal correlations. Since the computational complexity of PLCM is too high and cannot meet the real-time requirement, three strategies (bounding the number of secret pairs, limiting the maximum length of sessions and avoiding solving the subproblems repeatedly) are proposed to promote efficiency thirdly. At last, a privately continual release algorithm for web monitoring is presented based on the maximum privacy leakage calculated in the previous steps, which can reduce the computational complexity and the added noise significantly. Formal privacy analysis shows that our scheme satisfies ϵ-Pufferfish privacy. Extensive experiment results on the real-world dataset illustrate that our scheme outperforms other state-of-the-art techniques.