nils-herrmann/clean_scirepeval huggingface dataset:
Applying data mining techniques to address critical process optimization needs in advanced manufacturing

Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives.
 In this paper, we design, implement and deploy an integrated solution, named PDP-Miner, which is a data analytics platform customized for process optimization in Plasma Display Panel (PDP) manufacturing. The system utilizes the latest advances in data mining technologies and Big Data infrastructures to create a complete analytical solution. Besides, our proposed system is capable of supporting automatically configuring and scheduling analysis tasks, and balancing heterogeneous computing resources. The system and the analytic strategies can be applied to other advanced manufacturing fields to enable complex data analysis tasks. Since 2013, PDP-Miner has been deployed as the data analysis platform of ChangHong COC. By taking the advantages of our system, the overall PDP yield rate has increased from 91% to 94%. The monthly production is boosted by 10,000 panels, which brings more than 117 million RMB of revenue improvement per year.
Comparison of deep learning architectures for H&E histopathology images

Deep learning has achieved outstanding performance in many fields such as image classification and target recognition. Recently multiple research efforts are focusing on deep learning to medical image processing. While it is common in image processing to apply transfer learning for problems with small sample sizes, the statistics of histopathological stains are known to be very different from the photographic RGB images in common deep learning imaging tasks such as Imagenet and MIT Places. This paper evaluates the performance of fine-tuned models on Haematoxylin and Eosin(H&E) histopathology stain data. Furthermore, to analyze the performance of different deep learning architectures on these domains, we compare three convolutional neural network(CNN) architectures in various settings. Finally, the impact of the size of the context of training samples is evaluated. We use the BreaKHis dataset consisting of H&E stained microscopical scans of breast cancer tissue . Our results show that fine-tuned architectures perform favorably over neural networks that are trained from scratch in terms of accuracy and patient rate.
Ubii: Physical World Interaction Through Augmented Reality

We describe a new set of interaction techniques that allow users to interact with physical objects through augmented reality (AR). Previously, to operate a smart device, physical touch is generally needed and a graphical interface is normally involved. These become limitations and prevent the user from operating a device out of reach or operating multiple devices at once. Ubii (Ubiquitous interface and interaction) is an integrated interface system that connects a network of smart devices together, and allows users to interact with the physical objects using hand gestures. The user wears a smart glass which displays the user interface in an augmented reality view. Hand gestures are captured by the smart glass, and upon recognizing the right gesture input, Ubii will communicate with the connected smart devices to complete the designated operations. Ubii supports common inter-device operations such as file transfer, printing, projecting, as well as device pairing. To improve the overall performance of the system, we implement computation offloading to perform the image processing computation. Our user test shows that Ubii is easy to use and more intuitive than traditional user interfaces. Ubii shortens the operation time on various tasks involving operating physical devices. The novel interaction paradigm attains a seamless interaction between the physical and digital worlds.
Multi-hop Communication in the Uplink for LPWANs

Low-Power Wide Area Networks (LPWANs) have arisen as a promising communication technology for supporting Internet of Things (IoT) services due to their low power operation, wide coverage range, low cost and scalability. However, most LPWAN solutions like SIGFOXTMor LoRaWANTMrely on star topology networks, where stations (STAs) transmit directly to the gateway (GW), which often leads to rapid battery depletion in STAs located far from it. In this work, we analyze the impact on LPWANs energy consumption of multi-hop communication in the uplink, allowing STAs to transmit data packets in lower power levels and higher data rates to closer parent STAs, reducing their energy consumption consequently. To that aim, we introduce the DistanceRing Exponential Stations Generator (DRESG) framework, designed to evaluate the performance of the so-called optimal-hop routing model, which establishes optimal routing connections in terms of energy efficiency, aiming to balance the consumption among all the STAs in the network. Results show that enabling such multi-hop connections entails higher network lifetimes, reducing significantly the bottleneck consumption in LPWANs with up to thousands of STAs. These results lead to foresee multi-hop communication in the uplink as a promising routing alternative for extending the lifetime of LPWAN deployments.
How to write the methods section of a research paper.

The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the experimental design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. Material in each section should be organized by topic from most to least important.
A Complete End-to-End Speaker Verification System Using Deep Neural Networks: From Raw Signals to Verification Result

End-to-end systems using deep neural networks have been widely studied in the field of speaker verification. Raw audio signal processing has also been widely studied in the fields of automatic music tagging and speech recognition. However, as far as we know, end-to-end systems using raw audio signals have not been explored in speaker verification. In this paper, a complete end-to-end speaker verification system is proposed, which inputs raw audio signals and outputs the verification results. A pre-processing layer and the embedded speaker feature extraction models were mainly investigated. The proposed pre-emphasis layer was combined with a strided convolution layer for pre-processing at the first two hidden layers. In addition, speaker feature extraction models using convolutionallayer and long short-term memory are proposed to be embedded in the proposed end-to-end system.
Spatial big data analytics of influenza epidemic in Vellore, India

The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.
A Theory Of Instinctive Information Sharing

Recent psychology research suggests that people have an innate tendency to share information. This brings a new perspective to existing information sharing research. We elaborated a new theory with the central construct of need for information sharing. This need for information sharing is a measure of an individual’s innate need to share information with others. We also developed measurement items for this construct and empirically tested it in a survey on sharing of information in forums. The measurement items show good reliability and validity. The new construct can help to explain people’s intention to share information. The results support our new theory for information sharing behaviours.
Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting

Hong Kong Observatory has been operating an in-house developed rainfall nowcasting system called “Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS)” to support rainstorm warning and rainfall nowcasting services. A crucial step in rainfall nowcasting is the tracking of radar echoes to generate motion fields for extrapolation of rainfall areas in the following few hours. SWIRLS adopted a correlation-based method in its first operational version in 1999, which was subsequently replaced by optical flow algorithm in 2010 and further enhanced in 2013. The latest optical flow algorithm employs a transformation function to enhance a selected range of reflectivity for feature tracking. It also adopts variational optical flow computation that takes advantage of the Horn–Schunck approach and the Lucas–Kanade method. This paper details the three radar echo tracking algorithms, examines their performances in several significant rainstorm cases and summaries verification results of multi-year performances. The limitations of the current approach are discussed. Developments underway along with future research areas are also presented.
Robotics-logistics: Challenges for automation of logistic processes

Robotics-logistics can be understood as the field of activities in which applications of industrial robot-technologies are offered and demanded in order to ensure the optimization of internal material flows. According to a survey conducted by BIBA in 2007, the area of robotics-logistics has great need for modernization. Logistics activities can be classified in several scenarios as unloading / loading of goods and palletizing / depalletizing of goods. Possible scenarios for research and development activities within theses fields are given.
Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions.

Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.
A METHODOLOGY FOR REDESIGNING AN ORGANIZATIONAL STRUCTURE BASED ON BUSINESS PROCESS MODELS USING SNA TECHNIQUES

This paper proposes a methodology to derive an organizational structure that is suitable for business processes performed in an organization. The methodology uses social networks whose nodes are organizational units and whose arcs are transfers of work between two organizational units in terms of business processes. Social networks are analyzed using various SNA techniques. Redesigning guidelines are given using the analysis results. This methodology can be used to design an optimal organizational structure in terms of business processes and vice versa. A case study is presented to illustrate the validity and applicability of the methodology. This is also expected to contribute to enhancing the organizational performance.
Data Mining Methods for Recommender Systems

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.
High performance database logging using storage class memory

Storage class memory (SCM), a new generation of memory technology, offers non-volatility, high-speed, and byte-addressability, which combines the best properties of current hard disk drives (HDD) and main memory. With these extraordinary features, current systems and software stacks need to be redesigned to get significantly improved performance by eliminating disk input/output (I/O) barriers; and simpler system designs by avoiding complicated data format transformations. In current DBMSs, logging and recovery are the most important components to enforce the atomicity and durability of a database. Traditionally, database systems rely on disks for logging transaction actions and log records are forced to disks when a transaction commits. Because of the slow disk I/O speed, logging becomes one of the major bottlenecks for a DBMS. Exploiting SCM as a persistent memory for transaction logging can significantly reduce logging overhead. In this paper, we present the detailed design of an SCM-based approach for DBMSs logging, which achieves high performance by simplified system design and better concurrency support. We also discuss solutions to tackle several major issues arising during system recovery, including hole detection, partial write detection, and any-point failure recovery. This new logging approach is used to replace the traditional disk based logging approach in DBMSs. To analyze the performance characteristics of our SCM-based logging approach, we implement the prototype on IBM SolidDB. In common circumstances, our experimental results show that the new SCM-based logging approach provides as much as 7 times throughput improvement over disk-based logging in the Telecommunication Application Transaction Processing (TATP) benchmark.
Implementation of Improved Harmony Search Based Clustering Algorithm in Wireless Sensor Networks

The essential restriction in wireless sensor network (WSN) is constrained energy supply. The center of this paper is to put accentuation on building up a brought together, energy proficient clustering algorithm in light of Improved Harmony Search (IHS) to augment lifetime of sensor network and assessment of simulation situations its execution on a few WSN. The entire wireless sensor network is parceled into a few bunches by IHS algorithm. For streamlining, energy utilization of the nodes in WSN is the essential thought of actualizing the algorithm. The assessment of the IHS clustering algorithm against its counterparts was done on the premise of network lifetime and the measure of information sent to the Base Station (BS). Later on, the earlier practiced algorithms, for example, LEACH, LEACH-C and Cuckoo Search protocols were tried against the advocated protocol. KeywordsImproved Harmony Search (IHS), Wireless Sensor Network (WSN), Cuckoo Search (CS), Base Station (BS), Cluster Head (CH), Pitch Adjustment Rate (PAR), Bandwidth (bw).
Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure

The introduction of “smart grid” solutions imposes that cyber security and power system communication systems must be dealt with extensively. These parts together are essential for proper electricity transmission, where the information infrastructure is critical. The development of communication capabilities, moving power control systems from “islands of automation” to totally integrated computer environments, have opened up new possibilities and vulnerabilities. Since several power control systems have been procured with “openness” requirements, cyber security threats become evident. For refurbishment of a SCADA/EMS system, a separation of the operational and administrative computer systems must be obtained. The paper treats cyber security issues, and it highlights access points in a substation. Also, information security domain modeling is treated. Cyber security issues are important for “smart grid” solutions. Broadband communications open up for smart meters, and the increasing use of wind power requires a “smart grid system”.
AutoRec: A Recommender System Based on Social Media Stream

In this study, we employed Collaborative Filtering and Sentiment Analysis. This is to automatically produce a recommendation to consumers without requiring them to input ratings and feedback's. The researcher will be using Social media stream as test-bed which was currently being utilized by researchers around the globe to generate information which are useful in different fields. TwitterAPI will be employed to gather real time tweets to be stored in a database for analysis. Framework will be introduced to automatically produce a real time recommendation, which aims to test the effectiveness of the framework proposed by virtue of using DeLone and McClean IS Success Model. As a result the prototype datasets crawled from real world social media stream demonstrates that the proposed method depicts to be constructive. This study can contribute to the growing research projects on recommender system, data mining and to other relevant techniques.
Power analysis attack based on the MLP in DPA Contest v4

Power analysis represents extremely effective and successful way of side-channels attacks on so far confidential cryptographic algorithms and cryptographic devices. One of the widespread countermeasures against these power analysis attacks is the masking approach. In 2012, Nassar et al. presented a new lightweight masking countermeasure to protect the AES (Advanced Encryption Standard) implementation. This masking scheme represents the target algorithm of the DPA Contest v4. In this article, we present successful attack based on MLP (Multi-layer Perceptron) aimed on this masking countermeasure. For the first time, the MLP attack is used to reveal secret key from masked implementation of cryptographic algorithm. The article describes in detail every step of the attack implemented including the results achieved. Our attack reveal each byte of the secret key of the masked AES only with 23 power traces.
Facebook and texting made me do it: Media-induced task-switching while studying

Electronic communication is emotionally gratifying, but how do such technological distractions impact academic learning? The current study observed 263 middle school, high school and university students studying for 15 min in their homes. Observers noted technologies present and computer windows open in the learning environment prior to studying plus a minute-by-minute assessment of on-task behavior, off-task technology use and open computer windows during studying. A questionnaire assessed study strategies, task-switching preference, technology attitudes, media usage, monthly texting and phone calling, social networking use and grade point average (GPA). Participants averaged less than six minutes on task prior to switching most often due to technological distractions including social media, texting and preference for task-switching. Having a positive attitude toward technology did not affect being on-task during studying. However, those who preferred to task-switch had more distracting technologies available and were more likely to be off-task than others. Also, those who accessed Facebook had lower GPAs than those who avoided it. Finally, students with relatively high use of study strategies were more likely to stay on-task than other students. The educational implications include allowing students short ‘‘technology breaks’’ to reduce distractions and teaching students metacognitive strategies regarding when interruptions negatively impact learning.