nils-herrmann/clean_scirepeval huggingface dataset:
Deep Neural Networks for Generating Music

Deep learning has been used in many applications to solve real-world problems. In recent years, it has seen tremendous growth in its popularity and usefulness, due in large part to more powerful computers, larger datasets, and techniques to train deeper networks. The objective of this project is to explore deep learning regarding the field of music composition using artificial intelligence. We deepen a discussion about Recurrent Neural networks (RNNs), a type of neural network that has demonstrated the best results in music generation so far. After that, we use a Tied Parallel Network, which is a combination of a recurrent and a feedforward network. We modify the model to generate songs in such a manner that the rhythm is controlled accordingly to the speed of a person. Finally, we create a system to use the model in a real application. The system consists of a trained model capable of generating music indefinitely, an MP3 streaming server and an Android app that tracks the speed by GPS.
Gender Differences in Online Shoppers' Decision-Making Styles

Because of the SARS epidemic in Asia, people chose to the Internet shopping instead of going shopping on streets. In other words, SARS actually gave the Internet an opportunity to revive from its earlier bubbles. The purpose of this research is to provide managers of shopping Websites regarding consumer purchasing decisions based on the CSI (Consumer Styles Inventory) which was proposed by Sproles (1985) and Sproles & Kendall (1986). According to the CSI, one can capture the decision-making styles of online shoppers. Furthermore, this research also discusses the gender differences among online shoppers. Exploratory factor analysis (EFA) was used to understand the decision-making styles and discriminant analysis was used to distinguish the differences between female and male shoppers. Managers of Internet shopping Websites can design a proper marketing mix with the findings that there are differences in purchasing decisions between genders.
Fabrication and electrical characteristics of self-aligned (SA) gate-all-around (GAA) si nanowire MOSFETs (SNWFET)

We have proposed gate-all-around Silicon nanowire MOSFET (SNWFET) on bulk Si as an ultimate transistor. Well controlled processes are used to achieve gate length (LG) of sub-10nm and narrow nanowire widths. Excellent performance with reasonable VTH and short channel immunity are achieved owing to thin nanowire channel, self-aligned gate, and GAA structure. Transistor performance with gate length of 10nm has been demonstrated and nanowire size (DNW) dependency of various electrical characteristics has been investigated. Random telegraph noise (RTN) in SNWFET is studied as well.
Calendar Queues: A Fast 0(1) Priority Queue Implementation for the Simulation Event Set Problem

A new priority queue implementation for the future event set problem is described in this article. The new implementation is shown experimentally to be O(1) in queue size for the priority increment distributions recently considered by Jones in his review article. It displays hold times three times shorter than splay trees for a queue size of 10,000 events. The new implementation, called a calendar queue, is a very simple structure of the multiple list variety using a novel solution to the overflow problem.
IoT based smart home automation system using sensor node

In recent years, the advancements in Information and Communication Technology (ICT) are mainly focused on the Internet of Things (IoT). In a real-world scenario, IoT based services improve the domestic environment and are used in various applications. Home automation based IoT is versatile and popular applications. In home automation, all home appliances are networked together and able to operate without human involvement. Home automation gives a significant change in humans life which gives smart operating of home appliances. This motivated us to develop a new solution which controls some home appliances like light, fan, door cartons, energy consumption, and level of the Gas cylinder using various sensors like LM35, IR sensors, LDR module, Node MCU ESP8266, and Arduino UNO. The proposed solution uses the sensor and detects the presence or absence of a human object in the housework accordingly. Our solution also provides information about the energy consumed by the house owner regularly in the form of message. Also, it checks, the level of gas in the gas cylinder if it reaches lesser than the threshold, it automatically books the gas and sends a reference number as a message to the house owner. The proposed solution is deployed and tested for various conditions. Finally, in this paper, the working model of our proposed solution is developed as a prototype and explained as a working model.
Network forensics: Review, taxonomy, and open challenges

In recent years, a number of network forensics techniques have been proposed to investigate the increasing number of cybercrimes. Network forensics techniques assist in tracking internal and external network attacks by focusing on inherent network vulnerabilities and communication mechanisms. However, investigation of cybercrime becomes more challenging when cyber criminals erase the traces in order to avoid detection. Therefore, network forensics techniques employ mechanisms to facilitate investigation by recording every single packet and event that is disseminated into the network. As a result, it allows identification of the origin of the attack through reconstruction of the recorded data. In the current literature, network forensics techniques are studied on the basis of forensic tools, process models and framework implementations. However, a comprehensive study of cybercrime investigation using network forensics frameworks along with a critical review of present network forensics techniques is lacking. In other words, our study is motivated by the diversity of digital evidence and the difficulty of addressing numerous attacks in the network using network forensics techniques. Therefore, this paper reviews the fundamental mechanism of network forensics techniques to determine how network attacks are identified in the network. Through an extensive review of related literature, a thematic taxonomy is proposed for the classification of current network forensics techniques based on its implementation as well as target data sets involved in the conducting of forensic investigations. The critical aspects and significant features of the current network forensics techniques are investigated using qualitative analysis technique. We derive significant parameters from the literature for discussing the similarities and differences in existing network forensics techniques. The parameters include framework nature, mechanism, target dataset, target instance, forensic processing, time of investigation, execution definition, and objective function. Finally, open research challenges are discussed in network forensics to assist researchers in selecting the appropriate domains for further research and obtain ideas for exploring optimal techniques for investigating cyber-crimes. & 2016
Data mining in education

Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights.
Horizontal Fragmentation Technique in Distributed Database Ms .

Distributed database technology is expected to have a significant impact on data processing in the upcoming years. Today’s business environment has an increasing need for distributed database and Client/server applications as the desire for consistent, scalable, reliable and accessible information is steadily growing. Distributed processing is an effective way to improve reliability and performance of a database system. Distribution of data is a collection of fragmentation, allocation and replication processes. Previous research works provided fragmentation solution based on empirical data about the type and frequency of the queries submitted to a centralized system. These solutions are not suitable at the initial stage of a database design for a distributed system. The purpose of this work is to present an introduction to Distributed Databases which are becoming very popular now days with the description of distributed database environment, fragmentation and horizontal fragmentation technique. Horizontal fragmentation has an important impact in improving the applications performance that is strongly affected by distributed databases design phase. In this report, we have presented a fragmentation technique that can be applied at the initial stage as well as in later stages of a distributed database system for partitioning the relations. Allocation of fragments is done simultaneously in the algorithm. Result shows that proposed technique can solve initial fragmentation problem of relational databases for distributed systems properly.
Virtual reality and mixed reality for virtual learning environments

This paper explores educational uses of virtual learning environment (VLE) concerned with issues of learning, training and entertainment. We analyze the state-of-art research of VLE based on virtual reality and augmented reality. Some examples for the purpose of education and simulation are described. These applications show that VLE can be means of enhancing, motivating and stimulating learners’ understanding of certain events, especially those for which the traditional notion of instructional learning have proven inappropriate or difficult. Furthermore, the users can learn in a quick and happy mode by playing in the virtual environments.
Development, reliability, and validity of the Handwriting Proficiency Screening Questionnaire (HPSQ).

This study was used to (1) develop an occupational therapy screening questionnaire (the Handwriting Proficiency Screening Questionnaire [HPSQ]) to identify handwriting difficulties among school-aged children and (2) examine its reliability and validity.



The questionnaire's content validity was established. Internal consistency, interrater and test-retest reliability, and concurrent and construct validity were initiated. Participants included 7- to 14-year-old (N = 230) typically developing school-aged children.



The tool demonstrated good internal consistency (alpha = .90). Test-retest reliability for the score revealed an intraclass correlation coefficient (ICC) of .84 and interrater reliability of ICC = .92 for the total questionnaire score. Construct and concurrent validity were also confirmed.



The HPSQ is suitable for use by occupational therapists for identification of handwriting deficiency among school-aged children and is appropriate for varied academic and clinical uses. More studies with larger samples of varied age groups are required to further support the questionnaire's reliability and validity.
An Examination of Digital Forensic Models

Law enforcement is in a perpetual race with criminals in the application of digital technologies, and requires the development of tools to systematically search digital devices for pertinent evidence. Another part of this race, and perhaps more crucial, is the development of a methodology in digital forensics that encompasses the forensic analysis of all genres of digital crime scene investigations. This paper explores the development of the digital forensics process, compares and contrasts four particular forensic methodologies, and finally proposes an abstract model of the digital forensic procedure. This model attempts to address some of the shortcomings of previous methodologies, and provides the following advantages: a consistent and standardized framework for digital forensic tool development; a mechanism for applying the framework to future digital technologies; a generalized methodology that judicial members can use to relate technology to non-technical observers; and, the potential for incorporating nondigital electronic technologies within the abstractionmodel of the digital forensic procedure. This model attempts to address some of the shortcomings of previous methodologies, and provides the following advantages: a consistent and standardized framework for digital forensic tool development; a mechanism for applying the framework to future digital technologies; a generalized methodology that judicial members can use to relate technology to non-technical observers; and, the potential for incorporating nondigital electronic technologies within the abstraction  The digital age can be characterized as the application of computer technology as a tool that enhances traditional methodologies. The incorporation of computer systems as a tool into private, commercial, educational, governmental, and other facets of modern life has improved
The neural substrates of cognitive empathy.

Neuroscientific research has consistently found that the perception of an affective state in another activates the observer's own neural substrates for the corresponding state, which is likely the neural mechanism for "true empathy." However, to date there has not been a brain-imaging investigation of so-called "cognitive empathy", whereby one "actively projects oneself into the shoes of another person," imagining someone's personal, emotional experience as if it were one's own. In order to investigate this process, we conducted a combined psychophysiology and PET and study in which participants imagined: (1) a personal experience of fear or anger from their own past; (2) an equivalent experience from another person as if it were happening to them; and (3) a nonemotional experience from their own past. When participants could relate to the scenario of the other, they produced patterns of psychophysiological and neuroimaging activation equivalent to those of personal emotional imagery, but when they could not relate to the other's story, differences emerged on all measures, e.g., decreased psychophysiological responses and recruitment of a region between the inferior temporal and fusiform gyri. The substrates of cognitive empathy overlap with those of personal feeling states to the extent that one can relate to the state and situation of the other.
On stock return prediction with LSTM networks

Artificial neural networks are, again, on the rise. The decreasing costs of computing power and the availability of big data together with advancements of neural network theory have made this possible. In this thesis, LSTM (long short-term memory) recurrent neural networks are used in order to perform financial time series forecasting on return data of three stock indices. The indices are S&P 500 in the US, Bovespa 50 in Brazil and OMX 30 in Sweden. The results show that the outputs of the LSTM networks are very similar to those of a conventional time series model, namely an ARMA(1,1)-GJRGARCH(1,1), when a regression approach is taken. However, they outperform the time series model with regards to direction of change classification. The thesis shows significant results for direction of change classification for the small Swedish market, and insignificant results for the large US market and the emerging Brazilian market. When a trading strategy is implemented based on the direction of change, a deep LSTM network vastly outperforms the time series model.
Literature review of image features and classifiers used in leaf based plant recognition through image analysis approach

Plants play an important role in Earth's ecology by providing sustenance, shelter and maintaining a healthy atmosphere. Some of these plants have important medicinal properties. Automatic recognition of plant leaf is a challenging problem in the area of computer vision. An efficient Ayurvedic plant leaf recognition system will beneficial to many sectors of society which include medicinal field botanic research etc. With the help of image processing and pattern recognition, we can easily recognize the leaf images. This paper gives a survey on different leaf recognition methods and classifications. Plant leaf classification is a technique where leaf is classified based on its different features.
Predicting future personal life events on twitter via recurrent neural networks

Social network users publicly share a wide variety of information with their followers and the general public ranging from their opinions, sentiments and personal life activities. There has already been significant advance in analyzing the shared information from both micro (individual user) and macro (community level) perspectives, giving access to actionable insight about user and community behaviors. The identification of personal life events from user’s profiles is a challenging yet important task, which if done appropriately, would facilitate more accurate identification of users’ preferences, interests and attitudes. For instance, a user who has just broken his phone, is likely to be upset and also be looking to purchase a new phone. While there is work that identifies tweets that include mentions of personal life events, our work in this paper goes beyond the state of the art by predicting a future personal life event that a user will be posting about on Twitter solely based on the past tweets. We propose two architectures based on recurrent neural networks, namely the classification and generation architectures, that determine the future personal life event of a user. We evaluate our work based on a gold standard Twitter life event dataset and compare our work with the state of the art baseline technique for life event detection. While presenting performance measures, we also discuss the limitations of our work in this paper.
A Fault Detection and Reconfigurable Control Architecture for Unmanned Aerial Vehicles

The past decade has seen the development of several reconfigurable flight control strategies for unmanned aerial vehicles. Although the majority of the research is dedicated to fixed wing vehicles, simulation results do support the application of reconfigurable flight control to unmanned rotorcraft. This paper develops a fault tolerant control architecture that couples techniques for fault detection and identification with reconfigurable flight control to augment the reliability and autonomy of an unmanned aerial vehicle. The architecture is applicable to fixed and rotary wing aircraft. An adaptive neural network feedback linearization technique is employed to stabilize the vehicle after the detection of a fault. Actual flight test results support the validity of the approach on an unmanned helicopter. The fault tolerant control architecture recovers aircraft performance after the occurrence of four different faults in the flight control system: three swash-plate actuator faults and a collective actuator fault. All of these faults are catastrophic under nominal conditions
A Comparative Study of Image Steganography Techniques

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. From the past time to present time security of confidential information is always an important issue. The main purpose of stegnography is to hide the existence of the message so that it becomes difficult for attacker to detect it. Any type of cover object can be taken that may be text, image or video to embed the secret information. In this paper a brief analysis of different image stegnography techniques and their comparison is done.
Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification

We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation. Technically, we contribute a novel fully attentional block which is deeply supervised and can be plugged into any CNN, and a novel curriculum sampling method which is effective for training ranking losses. The learning tasks are integrated into a unified framework and jointly optimized. Experiments have been carried out on Market1501, CUHK03 and DukeMTMC. All the results show that Mancs can significantly outperform the previous state-of-the-arts. In addition, the effectiveness of the newly proposed ideas has been confirmed by extensive ablation studies.
BeatCoin : Leaking Private Keys from Air-Gapped

Cryptocurrency wallets store the wallets private key(s), and hence, are a lucrative target for attackers. With possession of the private key, an attacker virtually owns all of the currency in the compromised wallet. Managing cryptocurrency wallets offline, in isolated (’air-gapped’) computers, has been suggested in order to secure the private keys from theft. Such air-gapped wallets are often referred to as ’cold wallets.’ In this paper we show how private keys can be exfiltrated from air-gapped wallets. In the adversarial attack model, the attacker infiltrates the offline wallet, infecting it with malicious code. The malware can be preinstalled or pushed in during the initial installation of the wallet, or it can infect the system when removable media (e.g., USB flash drive) is inserted into the wallet’s computer in order to sign a transaction. These attack vectors have repeatedly been proven feasible in the last decade (e.g., [1],[2],[3],[4],[5],[6],[7],[8],[9],[10]). Having obtained a foothold in the wallet, an attacker can utilize various air-gap covert channel techniques (bridgeware [11]) to jump the airgap and exfiltrate the wallets private keys. We evaluate various exfiltration techniques, including physical, electromagnetic, electric, magnetic, acoustic, optical, and thermal techniques. This research shows that although cold wallets provide a high degree of isolation, its not beyond the capability of motivated attackers to compromise such wallets and steal private keys from them. We demonstrate how a 256-bit private key (e.g., bitcoin’s private keys) can be exfiltrated from an offline, air-gapped wallet of a fictional character named Satoshi within a matter of seconds.