nils-herrmann/clean_scirepeval huggingface dataset:
Development and characterization of new generation panel fan-out (P-FO) packaging technology

Wafer Level Packaging (WLP) is a packaging technology focusing on integrated circuit (IC) packaging at wafer level instead of die level. WLP essentially consists of IC foundry fabrication process and subsequent device interconnection and back-end passivation process. The general wafer level packages (WLPs) are designed for fan-in chip scale packaging but the shrinkage of pad pitch and size at the chip to package interface is much faster than the shrinkage at the package to board interface. The embedded package technologies are developed to provide larger package size in order to offer a sufficient area to accommodate the redistribution interconnection with standard pitches at the packaging. Embedded technology (FO-WLP / eWLB and embedded die) are unique technological breakthroughs enabling further growth of next-generation packaging modules and brings several benefits such as small form factor of packages, electrical and thermal performance improvement and cost reduction for IDMs. Compare to FO-WLP, panel level packaging (PLP) has the advantage of using large panel processing compare with FO-WLP while 12" is commonly used. Hence panel fan-out package (P-FO) has a better cost down potential than FO-WLP. To demonstrate the new generation panel fan-out package conception, the several hetero-technologies are integrated into the packaging which composed of PCB, LCD, Bumping and FOWLP has substantially broadened the spectrum of the well established wafer level packaging (WLP) technologies. The new generation panel fan-out packaging combines the different infrastructures from several fields to realize the conception. The achieved results for the package, such as the large panel size process and precise die alignment capability point to the outstanding potential of this novel hetero-system. In this paper, mixing PCB, semiconductor back-end, semiconductor WLP and LCD Gen 2.5 (370×470mm) size processing technologies combined with innovation as well as integration of P-FO techniques are proposed, including high accuracy die bonding and die shift compensation at film lamination, lower warpage sheet form film lamination, good copper trace plating uniformity control at large panel area and also precise photolithographic technique. In addition to technology elaboration and process depiction, relevant experimental data and final reliability test results on board level have also been thoroughly demonstrated and discussed.
ISO/IEC 27000, 27001 and 27002 for Information Security Management

With the increasing significance of information technology, there is an urgent need for adequate measures of information security. Systematic information security management is one of most important initiatives for IT management. At least since reports about privacy and security breaches, fraudulent accounting practices, and attacks on IT systems appeared in public, organizations have recognized their responsibilities to safeguard physical and information assets. Security standards can be used as guideline or framework to develop and maintain an adequate information security management system (ISMS). The standards ISO/IEC 27000, 27001 and 27002 are international standards that are receiving growing recognition and adoption. They are referred to as “common language of organizations around the world” for information security . With ISO/IEC 27001 companies can have their ISMS certified by a third-party organization and thus show their customers evidence of their security measures.
Review of the Principal Indicators and Data Science Techniques Used for the Detection of Financial Fraud and Money Laundering

Financial fraud and money laundering represent an important concern both nationally and worldwide due to the huge amounts of monetary losses they imply. Besides human inspection, Data Science (DS) has proved so far to be a useful tool in order to fight these activities by automatic means. Nevertheless, this approach to the problem is still in early stages, thus yielding plenty of field to be explored. This document discusses some of the principal variables/indicators and DS techniques that have been used in the recent years for detecting and describing fraudulent operations, to serve as a guide for future work in the field.
Blockchain application and outlook in the banking industry

Blockchain technology is a core, underlying technology with promising application prospects in the banking industry. On one hand, the banking industry in China is facing the impact of interest rate liberalization and profit decline caused by the narrowing interest-rate spread. On the other hand, it is also affected by economic transformation, Internet development, and financial innovations. Hence, the banking industry requires urgent transformation and is seeking new growth avenues. As such, blockchains could revolutionize the underlying technology of the payment clearing and credit information systems in banks, thus upgrading and transforming them. Blockchain applications also promote the formation of “multi-center, weakly intermediated” scenarios, which will enhance the efficiency of the banking industry. However, despite the permissionless and self-governing nature of blockchains, the regulation and actual implementation of a decentralized system are problems that remain to be resolved. Therefore, we propose the urgent establishment of a “regulatory sandbox” and the development of industry standards.
Principal components analysis corrects for stratification in genome-wide association studies

Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
Knowledge Extraction In Hadith Using Data Mining Technique

Muslims believe that the Sunnah of the Prophet Muha mm d(SAAW) is the second of the two revealed fundamental sources of Islam, after the Holy Qur'an . Hadith can provide a Gold Standard "ground truth" for AI (Artificial Intelligent) knowledge extraction and k nowledge representation experiments. In the present tudy, the extracted Islamic knowledge represented the focal p oint of the research, three famous books in Hadith science framed the corpus of the study. This study attempte d to explore new approach to classify Hadith using data mining techniques to classify Hadith according to i ts validity degree (Sahih, Hasan, Da'eef and Maudo' ), the proposed Hadith classifier model was built through learning process, DT classifier modeling had been represented by the tree structure model, and the at tribu es of the instances originally were obtained from the source books. Whilst some attributes were indicated as null values, or missing values. A novel mechan ism called missing data detector (MDD) was employed to handle these missing data. This mechanism was gener at d based on the Isnad validity methods in Hadith scien ce. The results of the research were compared with the resource books, concurrently with the point of view of the experts in the Hadith science. The findings of the research showed that the performance of DT Hadith c lassifier had significant effect with DDM, the CCR was sharply increased from (50.1502 %) to (97.597%) Fur the more, the favorable obtained results indicated hat the DT Modeling is a viable approach to classify Ha dith due to the ease of rules induction and results
Multi-labelled classification using maximum entropy method

Many classification problems require classifiers to assign each single document into more than one category, which is called multi-labelled classification. The categories in such problems usually are neither conditionally independent from each other nor mutually exclusive, therefore it is not trivial to directly employ state-of-the-art classification algorithms without losing information of relation among categories. In this paper, we explore correlations among categories with maximum entropy method and derive a classification algorithm for multi-labelled documents. Our experiments show that this method significantly outperforms the combination of single label approach.
From genetic privacy to open consent

Recent advances in high-throughput genomic technologies are showing concrete results in the form of an increasing number of genome-wide association studies and in the publication of comprehensive individual genome–phenome data sets. As a consequence of this flood of information the established concepts of research ethics are stretched to their limits, and issues of privacy, confidentiality and consent for research are being re-examined. Here, we show the feasibility of the co-development of scientific innovation and ethics, using the open-consent framework that was implemented in the Personal Genome Project as an example.
Taxonomy of attacks on industrial control protocols

Industrial control systems (ICS) are highly distributed information systems used to control and monitor critical infrastructures such as nuclear plants, power generation and distribution plants, Oil and Gas and many other facilities. The main architecture principles of ICS are; real time response, high availability and reliability. For these specific purposes, several protocols has been designed to ensure the control and supervision operations. Modbus and DNP3 are the most used protocols in the ICS world due to their compliance with real time needs. With the increasing of the connectivity to the internet world for business reasons, ICS adopted Internet based technologies and most of communication protocols are redesigned to work over IP. This openness exposed the ICS components as well as communication protocols to cyber-attacks with a higher risk than attacks on traditional IT systems. In order to facilitate the risk assessment of cyber-attacks on ICS protocols we propose a taxonomy model of different identified attacks on Modbus and DNP3.the model is based on the threat origin, threat type, attack type, attack scenario, vulnerability type and the impact of the attack. We populate this Taxonomy model with identified attacks on Modbus and DNP3 from previous academic and industrial works.
Is psychology suffering from a replication crisis? What does "failure to replicate" really mean?

Psychology has recently been viewed as facing a replication crisis because efforts to replicate past study findings frequently do not show the same result. Often, the first study showed a statistically significant result but the replication does not. Questions then arise about whether the first study results were false positives, and whether the replication study correctly indicates that there is truly no effect after all. This article suggests these so-called failures to replicate may not be failures at all, but rather are the result of low statistical power in single replication studies, and the result of failure to appreciate the need for multiple replications in order to have enough power to identify true effects. We provide examples of these power problems and suggest some solutions using Bayesian statistics and meta-analysis. Although the need for multiple replication studies may frustrate those who would prefer quick answers to psychology's alleged crisis, the large sample sizes typically needed to provide firm evidence will almost always require concerted efforts from multiple investigators. As a result, it remains to be seen how many of the recently claimed failures to replicate will be supported or instead may turn out to be artifacts of inadequate sample sizes and single study replications.
Exploring consumers' impulse buying behavior on social commerce platform: The role of parasocial interaction

Social commerce, a recent branch of e-commerce, has made the experience of consumers on social commerce platform (SCP) different from other contexts, as the consumers have social interactions with each other. Growing evidence also shows that consumers on these platforms are prone to impulse buying behavior owing to the social interactions. However, existing research on online impulse buying was limited when illustrating the behavior of consumers on SCPs as social relationship constructs were not included. New theoretical developments are needed in regard to fill the research gap. In this research, parasocial interaction (PSI) theory is introduced in this research to examine the influence of social relationship factors on the formation of impulse buying behavior. An empirical research has been conducted mpulse buying tendency on Mogujie (www.mogujie.com), one of the most popular image-sharing SCPs in China. Results indicate that PSI exerts an impact on impulse buying tendency, the social-relevant features of the SCP determine PSI, and perceived usefulness and PSI both significantly affect perceived enjoyment. In addition, consumers’ perceived enjoyment and impulse buying tendencies significantly affect their urge to buy impulsively. The implications, limitations, and discussions are provided.
A utility framework for the automatic generation of audio-visual skims

In this paper, we present a novel algorithm for generating audio-visual skims from computable scenes. Skims are useful for browsing digital libraries, and for on-demand summaries in set-top boxes. A computable scene is a chunk of data that exhibits consistencies with respect to chromaticity, lighting and sound. There are three key aspects to our approach: (a) visual complexity and grammar, (b) robust audio segmentation and (c) an utility model for skim generation. We define a measure of visual complexity of a shot, and map complexity to the minimum time for comprehending the shot. Then, we analyze the underlying visual grammar, since it makes the shot sequence meaningful. We segment the audio data into four classes, and then detect significant phrases in the speech segments. The utility functions are defined in terms of complexity and duration of the segment. The target skim is created using a general constrained utility maximization procedure that maximizes the information content and the coherence of the resulting skim. The objective function is constrained due to multimedia synchronization constraints, visual syntax and by penalty functions on audio and video segments. The user study results indicate that the optimal skims show statistically significant differences with other skims with compression rates up to 90%.
Biometric classifier update using online learning: A case study in near infrared face verification

The performance of large-scale biometric system deteriorates over time as new individuals are continually enrolled. To maintain a high level of performance, the classifier has to be re-trained offline in batch mode using both the existing and new data. The process of re-training can be computationally expensive and time consuming. This paper presents a new biometric classifier update algorithm that incrementally re-trains the classifier using online learning and progressively establishes a decision hyperplane for improved classification. The proposed algorithm incorporates soft labels and granular computing in the formulation of 2ν-Online Granular Soft Support Vector Machine to re-train the classifier using only the new data. Granular computing makes it adaptive to local and global variations in data distribution, while soft labels provide resilience to noise. Each time a data is acquired, new support vectors that are linearly independent are added and existing support vectors that do not improve the classifier performance are removed. This constrains the size of the support vectors and significantly reduces the training time without compromising the classification accuracy. The efficacy of the proposed online learning strategy is validated in a near-infrared face verification application involving different covariates. The results obtained on a heterogeneous near-infrared face database of 328 subjects show that in all experiments using different feature extraction and classification algorithms the proposed online 2ν-Granular Soft Support Vector Machine learning approach is 2-3 times faster while achieving a high level of accuracy similar to offline training using all data.
Frontal midline EEG dynamics during working memory

We show that during visual working memory, the electroencephalographic (EEG) process producing 5-7 Hz frontal midline theta (fmtheta) activity exhibits multiple spectral modes involving at least three frequency bands and a wide range of amplitudes. The process accounting for the fmtheta increase during working memory was separated from 71-channel data by clustering on time/frequency transforms of components returned by independent component analysis (ICA). Dipole models of fmtheta component scalp maps were consistent with their generation in or near dorsal anterior cingulate cortex. From trial to trial, theta power of fmtheta components varied widely but correlated moderately with theta power in other frontal and left temporal processes. The weak mean increase in frontal midline theta power with increasing memory load, produced entirely by the fmtheta components, largely reflected progressively stronger theta activity in a relatively small proportion of trials. During presentations of letter series to be memorized or ignored, fmtheta components also exhibited 12-15 Hz low-beta activity that was stronger during memorized than during ignored letter trials, independent of letter duration. The same components produced a brief 3-Hz burst 500 ms after onset of the Probe letter following each letter sequence. A new decomposition method, log spectral ICA, applied to normalized log time/frequency transforms of fmtheta component Memorize-letter trials, showed that their low-beta activity reflected harmonic energy in continuous, sharp-peaked theta wave trains as well as independent low-beta bursts. Possibly, the observed fmtheta process variability may index dynamic adjustments in medial frontal cortex to trial-specific behavioral context and task demands.
An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech

In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve the intelligibility of noisy unprocessed speech. In this paper, a short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time-frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments. In general, STOI showed better correlation with speech intelligibility compared to five other reference objective intelligibility models. In contrast to other conventional intelligibility models which tend to rely on global statistics across entire sentences, STOI is based on shorter time segments (386 ms). Experiments indeed show that it is beneficial to take segment lengths of this order into account. In addition, a free Matlab implementation is provided.
Hydrothermal liquefaction of biomass: developments from batch to continuous process.

This review describes the recent results in hydrothermal liquefaction (HTL) of biomass in continuous-flow processing systems. Although much has been published about batch reactor tests of biomass HTL, there is only limited information yet available on continuous-flow tests, which can provide a more reasonable basis for process design and scale-up for commercialization. High-moisture biomass feedstocks are the most likely to be used in HTL. These materials are described and results of their processing are discussed. Engineered systems for HTL are described; however, they are of limited size and do not yet approach a demonstration scale of operation. With the results available, process models have been developed, and mass and energy balances determined. From these models, process costs have been calculated and provide some optimism as to the commercial likelihood of the technology.
Applying DDDAS Principles to Command, Control and Mission Planning for UAV Swarms

Government agencies predict ever-increasing inventories of Unmanned Aerial Vehicles (UAVs). Sizes will vary from current manned aircraft scales to miniature, micro, millimeter scales or smaller. Missions for UAVs will increase, especially for the 3-D missions: dull, dirty, and dangerous. As their numbers and missions increase, three important challenges will emerge for these large swarms of sensor and surveillance UAVs: (1) the need for near real-time dynamic command & control of the swarms, (2) efficient mission planning and dynamic real-time re-tasking of the swarms, and 3) the need for improved automation of swarm mission planning and command & control. We describe an investigation with the primary objectives to design, develop, and evaluate: (i) a proof-of-concept simulation test-bed that investigates the benefits of using DDDAS (Dynamic Data Driven Applications Systems) for UAV swarm control, and (ii) engineering guidelines that will enable the use of DDDAS principles in such actual systems.
Process mining: a two-step approach to balance between underfitting and overfitting

Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such “overfitting” by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about completeness. As a result, parts of the model are “overfitting” (allow only for what has actually been observed) while other parts may be “underfitting” (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between “overfitting” and “underfitting”. To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the “theory of regions”, the model is synthesized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.
Real-time pen-and-ink illustration of landscapes

Non-photorealistic rendering has been proven to be particularly efficient in conveying and transmitting selected visual information. Our paper presents a NPR rendering pipeline that supports pen-and-ink illustration for, but not limited to, complex landscape scenes in real time. This encompasses a simplification framework using clustering which enables new approaches to efficient and coherent rendering of stylized silhouettes, hatching and abstract shading. Silhouette stylization is performed in image-space. This avoids explicit computation of connected lines. Further, coherent hatching of the tree foliage is performed using an approximate view-dependent parameterization computed on-the-fly within the same simplification framework. All NPR algorithms are integrated with photorealistic rendering, allowing seamless transition and combination between a variety of photorealistic and non-photorealistic drawing styles.