nils-herrmann/clean_scirepeval huggingface dataset:
Role of bacterial biofertilizers in agriculture and forestry

Many rhizospheric bacterial strains possess plant growth-promoting mechanisms. These bacteria can be applied as biofertilizers in agriculture and forestry, enhancing crop yields. Bacterial biofertilizers can improve plant growth through several different mechanisms: (i) the synthesis of plant nutrients or phytohormones, which can be absorbed by plants, (ii) the mobilization of soil compounds, making them available for the plant to be used as nutrients, (iii) the protection of plants under stressful conditions, thereby counteracting the negative impacts of stress, or (iv) defense against plant pathogens, reducing plant diseases or death. Several plant growth-promoting rhizobacteria (PGPR) have been used worldwide for many years as biofertilizers, contributing to increasing crop yields and soil fertility and hence having the potential to contribute to more sustainable agriculture and forestry. The technologies for the production and application of bacterial inocula are under constant development and improvement and the bacterial-based biofertilizer market is growing steadily. Nevertheless, the production and application of these products is heterogeneous among the different countries in the world. This review summarizes the main bacterial mechanisms for improving crop yields, reviews the existing technologies for the manufacture and application of beneficial bacteria in the field, and recapitulates the status of the microbe-based inoculants in World
Hybrid Genetic Algorithm with K-Means for Clustering Problems

The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.
Search-based software test data generation: a survey

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that in general, test data generation is an undecidable problem. Metaheuristic search techniques offer much promise in regard to these problems. Metaheuristic search techniques are highlevel frameworks, which utilise heuristics to seek solutions for combinatorial problems at a reasonable computational cost. To date, metaheuristic search techniques have been applied to automate test data generation for structural and functional testing; the testing of grey-box properties, for example safety constraints; and also non-functional properties, such as worst-case execution time. This paper surveys some of the work undertaken in this field, discussing possible new future directions of research for each of its different individual areas.
Data Mining Yelp Data-Predicting rating stars from review text

The majority of the online reviews are written in free-text format. It is often useful to have a measure which summarizes the content of the review. One such measure can be sentiment which expresses the polarity (positive/negative) of the review. However, a more granular classification of sentiment, such as rating stars, would be more advantageous and would help the user form a better opinion. In this project, we propose an approach which involves a combination of topic modeling and sentiment analysis to achieve this objective and thereby help predict the rating stars.
Interactions of herbs with cytochrome P450.

A resurgence in the use of medical herbs in the Western world, and the co-use of modern and traditional therapies is becoming more common. Thus there is the potential for both pharmacokinetic and pharmacodynamic herb-drug interactions. For example, systems such as the cytochrome P450 (CYP) may be particularly vulnerable to modulation by the multiple active constituents of herbs, as it is well known that the CYPs are subject to induction and inhibition by exposure to a wide variety of xenobiotics. Using in vitro, in silico, and in vivo approaches, many herbs and natural compounds isolated from herbs have been identified as substrates, inhibitors, and/or inducers of various CYP enzymes. For example, St. John's wort is a potent inducer of CYP3A4, which is mediated by activating the orphan pregnane X receptor. It also contains ingredients that inhibit CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. Many other common medicinal herbs also exhibited inducing or inhibiting effects on the CYP system, with the latter being competitive, noncompetitive, or mechanism-based. It appears that the regulation of CYPs by herbal products complex, depending on the herb type, their administration dose and route, the target organ and species. Due to the difficulties in identifying the active constituents responsible for the modulation of CYP enzymes, prediction of herb-drug metabolic interactions is difficult. However, herb-CYP interactions may have important clinical and toxicological consequences. For example, induction of CYP3A4 by St. John's wort may partly provide an explanation for the enhanced plasma clearance of a number of drugs, such as cyclosporine and innadivir, which are known substrates of CYP3A4, although other mechanisms including modulation of gastric absorption and drug transporters cannot be ruled out. In contrast, many organosulfur compounds, such as diallyl sulfide from garlic, are potent inhibitors of CYP2E1; this may provide an explanation for garlic's chemoproventive effects, as many mutagens require activation by CYP2E1. Therefore, known or potential herb-CYP interactions exist, and further studies on their clinical and toxicological roles are warranted. Given that increasing numbers of people are exposed to a number of herbal preparations that contain many constituents with potential of CYP modulation, high-throughput screening assays should be developed to explore herb-CYP interactions.
Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts

Named entity recognition (NER) is an important task in NLP, which is all the more challenging in conversational domain with their noisy facets. Moreover, conversational texts are often available in limited amount, making supervised tasks infeasible. To learn from small data, strong inductive biases are required. Previous work relied on hand-crafted features to encode these biases until transfer learning emerges. Here, we explore a transfer learning method, namely language model pretraining, on NER task in Indonesian conversational texts. We utilize large unlabeled data (generic domain) to be transferred to conversational texts, enabling supervised training on limited in-domain data. We report two transfer learning variants, namely supervised model fine-tuning and unsupervised pretrained LM fine-tuning. Our experiments show that both variants outperform baseline neural models when trained on small data (100 sentences), yielding an absolute improvement of 32 points of test F1 score. Furthermore, we find that the pretrained LM encodes part-of-speech information which is a strong predictor for NER.
The Interplay between Form, Story, and History : The Use of Narrative in Cultural and Educational Virtual Reality

This paper attempts to review examples of the use of storytelling and narrative in immersive virtual reality worlds. Particular attention is given to the way narrative is incorporated in artistic, cultural, and educational applications through the development of specific sensory and perceptual experiences that are based on characteristics inherent to virtual reality, such as immersion, interactivity, representation, and illusion. Narrative development is considered on three axes: form (visual representation), story (emotional involvement), and history (authenticated cultural content) and how these can come together.
HIPI : A Hadoop Image Processing Interface for Image-based MapReduce Tasks

The amount of images being uploaded to the internet is rapidly increasing, with Facebook users uploading over 2.5 billion new photos every month [Facebook 2010], however, applications that make use of this data are severely lacking. Current computer vision applications use a small number of input images because of the difficulty is in acquiring computational resources and storage options for large amounts of data [Guo. . . 2005; White et al. 2010]. As such, development of vision applications that use a large set of images has been limited [Ghemawat and Gobioff. . . 2003]. The Hadoop Mapreduce platform provides a system for large and computationally intensive distributed processing (Dean, 2004), though use of Hadoops system is severely limited by the technical complexities of developing useful applications [Ghemawat and Gobioff. . . 2003; White et al. 2010]. To immediately address this, we propose an open-source Hadoop Image Processing Interface (HIPI) that aims to create an interface for computer vision with MapReduce technology. HIPI abstracts the highly technical details of Hadoop’s system and is flexible enough to implement many techniques in current computer vision literature. This paper describes the HIPI framework, and describes two example applications that have been implemented with HIPI. The goal of HIPI is to create a tool that will make development of large-scale image processing and vision projects extremely accessible in hopes that it will empower researchers and students to create applications with ease.
Nethammer: Inducing Rowhammer Faults through Network Requests

A fundamental assumption in software security is that memory contents do not change unless there is a legitimate deliberate modification. Classical fault attacks show that this assumption does not hold if the attacker has physical access. Rowhammer attacks showed that local code execution is already sufficient to break this assumption. Rowhammer exploits parasitic effects in DRAM tomodify the content of a memory cell without accessing it. Instead, other memory locations are accessed at a high frequency. All Rowhammer attacks so far were local attacks, running either in a scripted language or native code. In this paper, we present Nethammer. Nethammer is the first truly remote Rowhammer attack, without a single attacker-controlled line of code on the targeted system. Systems that use uncached memory or flush instructions while handling network requests, e.g., for interaction with the network device, can be attacked using Nethammer. Other systems can still be attacked if they are protected with quality-of-service techniques like Intel CAT. We demonstrate that the frequency of the cache misses is in all three cases high enough to induce bit flips. We evaluated different bit flip scenarios. Depending on the location, the bit flip compromises either the security and integrity of the system and the data of its users, or it can leave persistent damage on the system, i.e., persistent denial of service. We investigated Nethammer on personal computers, servers, and mobile phones. Nethammer is a security landslide, making the formerly local attack a remote attack. With this work we invalidate all defenses and mitigation strategies against Rowhammer build upon the assumption of a local attacker. Consequently, this paradigm shift impacts the security of millions of devices where the attacker is not able to execute attacker-controlled code. Nethammer requires threat models to be re-evaluated for most network-connected systems. We discuss state-of-the-art countermeasures and show that most of them have no effect on our attack, including the targetrow-refresh (TRR) countermeasure of modern hardware.  This work on Rowhammer attacks over the network was conducted independently and unaware of other research groups working on truly remote Rowhammer attacks. Experiments and observations presented in this paper, predate the publication of the Throwhammer attack by Tatar et al. . We will thoroughly study the differences between both papers and compare the advantages and disadvantages in a future version of this paper.
Probabilistic video stabilization using Kalman filtering and mosaicking

The removal of unwanted, parasitic vibrations in a video sequence induced by camera motion is an essential part of video acquisition in industrial, military and consumer applications. In this paper, we present a new image processing method to remove such vibrations and reconstruct a video sequence void of sudden camera movements. Our approach to separating unwanted vibrations from intentional camera motion is based on a probabilistic estimation framework. We treat estimated parameters of interframe camera motion as noisy observations of the intentional camera motion parameters. We construct a physics-based state-space model of these interframe motion parameters and use recursive Kalman filtering to perform stabilized camera position estimation. A six-parameter affine model is used to describe the interframe transformation, allowing quite accurate description of typical scene changes due to camera motion. The model parameters are estimated using a p-norm-based multi-resolution approach. This approach is robust to model mismatch and to object motion within the scene (which are treated as outliers). We use mosaicking in order to reconstruct undefined areas that result from motion compensation applied to each video frame. Registration between distant frames is performed efficiently by cascading interframe affine transformation parameters. We compare our method's performance with that of a commercial product on real-life video sequences, and show a significant improvement in stabilization quality for our method.
Data Auditing and Security in Cloud Computing : Issues , Challenges and Future Directions

Cloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discussed.
Pricing Approaches for Data Markets

Currently, multiple data vendors utilize the cloud-computing paradigm for trading raw data, associated analytical services, and analytic results as a commodity good. We observe that these vendors often move the functionality of data warehouses to cloud-based platforms. On such platforms, vendors provide services for integrating and analyzing data from public and commercial data sources. We present insights from interviews with seven established vendors about their key challenges with regard to pricing strategies in different market situations and derive associated research problems for the business intelligence community.
Ontop: Answering SPARQL queries over relational databases

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL 2 QL and RDFS ontologies), and its support for all major relational databases.
Segmentation Strategies for Streaming Speech Translation

The study presented in this work is a first effort at real-time speech translation of TED talks, a compendium of public talks with different speakers addressing a variety of topics. We address the goal of achieving a system that balances translation accuracy and latency. In order to improve ASR performance for our diverse data set, adaptation techniques such as constrained model adaptation and vocal tract length normalization are found to be useful. In order to improve machine translation (MT) performance, techniques that could be employed in real-time such as monotonic and partial translation retention are found to be of use. We also experiment with inserting text segmenters of various types between ASR and MT in a series of real-time translation experiments. Among other results, our experiments demonstrate that a good segmentation is useful, and a novel conjunction-based segmentation strategy improves translation quality nearly as much as other strategies such as comma-based segmentation. It was also found to be important to synchronize various pipeline components in order to minimize latency.
How Good Are My Tests?

Test quality is a prerequisite for achieving production system quality. While the concept of quality is multidimensional, most of the effort in testing context hasbeen channelled towards measuring test effectiveness.  While effectiveness of tests is certainly important, we aim to identify a core list of testing principles that also address other quality facets of testing, and to discuss how they can be quantified as indicators of test quality.  We have conducted a two-day workshop with our industry partners to come up with a list of relevant principles and best practices expected to result in high quality tests. We then utilised our academic and industrial training materials together with recommendations in practitioner oriented testing books to refine the list. We surveyed existing literature for potential metrics to quantify identified principles.  We have identified a list of 15 testing principles to capture the essence of testing goals and best practices from quality perspective. Eight principles do not map toexisting test smells and we propose metrics for six of those. Further, we have identified additional potential metrics for the seven principles that partially map to test smells.  We provide a core list of testing principles along with a discussion of possible ways to quantify them for assessing goodness of tests. We believe that our work wouldbe useful for practitioners in assessing the quality of their tests from multiple perspectives including but not limited to maintainability, comprehension and simplicity.
Nuclear Envelope Protein SUN2 Promotes Cyclophilin-A-Dependent Steps of HIV Replication

During the early phase of replication, HIV reverse transcribes its RNA and crosses the nuclear envelope while escaping host antiviral defenses. The host factor Cyclophilin A (CypA) is essential for these steps and binds the HIV capsid; however, the mechanism underlying this effect remains elusive. Here, we identify related capsid mutants in HIV-1, HIV-2, and SIVmac that are restricted by CypA. This antiviral restriction of mutated viruses is conserved across species and prevents nuclear import of the viral cDNA. Importantly, the inner nuclear envelope protein SUN2 is required for the antiviral activity of CypA. We show that wild-type HIV exploits SUN2 in primary CD4+ T cells as an essential host factor that is required for the positive effects of CypA on reverse transcription and infection. Altogether, these results establish essential CypA-dependent functions of SUN2 in HIV infection at the nuclear envelope.
Beyond behaviorism: on the automaticity of higher mental processes.

The first 100 years of experimental psychology were dominated by 2 major schools of thought: behaviorism and cognitive science. Here the authors consider the common philosophical commitment to determinism by both schools, and how the radical behaviorists' thesis of the determined nature of higher mental processes is being pursued today in social cognition research on automaticity. In harmony with "dual process" models in contemporary cognitive science, which equate determined processes with those that are automatic and which require no intervening conscious choice or guidance, as opposed to "controlled" processes which do, the social cognition research on the automaticity of higher mental processes provides compelling evidence for the determinism of those processes. This research has revealed that social interaction, evaluation and judgment, and the operation of internal goal structures can all proceed without the intervention of conscious acts of will and guidance of the process.
Understanding the Performance and Potential of Cloud Computing for Scientific Applications

Commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems. Cloud computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work provides a comprehensive evaluation of EC2 cloud in different aspects. We first analyze the potentials of the cloud by evaluating the raw performance of different services of AWS such as compute, memory, network and I/O. Based on the findings on the raw performance, we then evaluate the performance of the scientific applications running in the cloud. Finally, we compare the performance of AWS with a private cloud, in order to find the root cause of its limitations while running scientific applications. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud in terms of both raw performance and scientific applications performance. Furthermore, we evaluate other services including S3, EBS and DynamoDB among many AWS services in order to assess the abilities of those to be used by scientific applications and frameworks. We also evaluate a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.
An Interaction Design Method for Creative Conceptual Models' Design

How to develop creative digital products which really meet the prosumer's needs while promoting a positive user experience? That question has guided this work looking for answers through different disciplinary fields. Born on 2002 as an Engineering PhD dissertation, since 2003 the method has been improved by teaching it to Communication and Design graduate and undergraduate courses. It also guided some successful interdisciplinary projects. Its main focus is on developing a creative conceptual model that might meet a human need within its context. The resulting method seeks: (1) solutions for the main problems detected in the previous versions; (2) significant ways to represent Design practices; (3) a set of activities that could be developed by people without programming knowledge. The method and its research current state are presented in this work.