nils-herrmann/clean_scirepeval huggingface dataset:
Kaleido: You Can Watch It But Cannot Record It

Recently a number of systems have been developed to implement and improve the visual communication over screen-camera links. In this paper we study an opposite problem: how to prevent unauthorized users from videotaping a video played on a screen, such as in a theater, while do not affect the viewing experience of legitimate audiences. We propose and develop a light-weight hardware-free system, called Kaleido, that ensures these properties by taking advantage of the limited disparities between the screen-eye channel and the screen-camera channel. Kaleido does not require any extra hardware and is purely based on re-encoding the original video frame into multiple frames used for displaying. We extensively test our system Kaleido using a variety of smartphone cameras. Our experiments confirm that Kaleido preserves the high-quality screen-eye channel while reducing the secondary screen-camera channel quality significantly.
Behavioral Addiction versus Substance Addiction: Correspondence of Psychiatric and Psychological Views

Behavioral science experts believe that all entities capable of stimulating a person can be addictive; and whenever a habit changes into an obligation, it can be considered as an addiction. Researchers also believe that there are a number of similarities as well as some differences between drug addiction and behavioral addiction diagnostic symptoms. The purpose of this study is to consider different approaches in this field.



This is a descriptive research using content analysis method. First, differences and similarities of various perspectives on addiction and addiction behavior in different substances were obtained, thereafter, the data was coded and categorized, subjects were discussed and major issues were extracted.



Behavioral addiction such as internet addiction is similar to drug addiction except that in the former, the individual is not addicted to a substance but the behavior or the feeling brought about by the relevant action. In addition, the physical signs of drug addiction, are absent in behavioral addiction. Others have stated that behaviorally addicted individuals have certain symptoms and will undergo the same consequences brought about by addiction to alcohol and drugs as well as other obsessive behaviors.



Similar to substance abuse prevention, programs aimed at addicted individuals and specialized training can educate adolescents about the warning signs of online addiction, in order to assist the early detection of this disorder. For prevention of behavioral addiction (such as internet addiction) authorities, cultural institutions and parents should monitor the use of internet and teach to the adolescent and children, the useful and appropriate methods of internet use.
Neurobiological Basis of Language Learning Difficulties

In this paper we highlight why there is a need to examine subcortical learning systems in children with language impairment and dyslexia, rather than focusing solely on cortical areas relevant for language. First, behavioural studies find that children with these neurodevelopmental disorders perform less well than peers on procedural learning tasks that depend on corticostriatal learning circuits. Second, fMRI studies in neurotypical adults implicate corticostriatal and hippocampal systems in language learning. Finally, structural and functional abnormalities are seen in the striatum in children with language disorders. Studying corticostriatal networks in developmental language disorders could offer us insights into their neurobiological basis and elucidate possible modes of compensation for intervention.
Negative pressure wave based pipeline Leak Detection: Challenges and algorithms

Negative pressure wave is a popular method to detect the occurrence and location of leak incidents in oil/gas pipeline. Three core technical challenges and related algorithm are discussed in this paper. The first is data quality. The balance between noise level and locating precision is discussed in filter design. The second one is dynamic slope in anomaly detection, whence a bi-SPC (Static Process Control) algorithms is proposed to make the threshold be adaptive. The third one is the false alarm caused normal working condition changes. Multiple-sensor paring algorithms is presented. With these algorithms, the robustness and locating precision of LDS (Leak Detection System) can be ensured.
Improving Intrusion Detection System based on Snort rules for network probe attack detection

Data and network system security is the most important roles. An organization should find the methods to protect their data and network system to reduce the risk from attacks. Snort Intrusion Detection System (Snort-IDS) is a security tool of network security. It has been widely used for protecting the network of the organizations. The Snort-IDS utilize the rules to matching data packets traffic. If some packet matches the rules, Snort-IDS will generate the alert messages. However, Snort-IDS contain many rules and it also generates a lot of false alerts. In this paper, we present the procedure to improve the Snort-IDS rules for the network probe attack detection. In order to test the performance evaluation, we utilized the data set from the MIT-DAPRA 1999, which includes the normal and abnormal traffics. Firstly, we analyzed and explored the existing the Snort-IDS rules to improve the proposed Snort-IDS rules. Secondly, we applied the WireShark software to analyze data packets form of attack in data set. Finally, the Snort-IDS was improved, and it can detect the network probe attack. This paper, we had classified the attacks into several groups based on the nature of network probe attack. In addition, we also compared the efficacy of detection attacks between Snort-IDS rules to be updated with the Detection Scoring Truth. As the experimental results, the proposed Snort-IDS efficiently detected the network probe attacks compared to the Detection Scoring Truth. It can achieve higher accuracy. However, there were some detecting alert that occur over the attack in Detection Scoring Truth, because some attack occur in several time but the Detection Scoring Truth indentify as one time.
Simple to complex cross-modal learning to rank

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model’s robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the stateof-the-arts in this literature.
Unfolding the role of protein misfolding in neurodegenerative diseases

Recent evidence indicates that diverse neurodegenerative diseases might have a common cause and pathological mechanism — the misfolding, aggregation and accumulation of proteins in the brain, resulting in neuronal apoptosis. Studies from different disciplines strongly support this hypothesis and indicate that a common therapy for these devastating disorders might be possible. The aim of this article is to review the literature on the molecular mechanism of protein misfolding and aggregation, its role in neurodegeneration and the potential targets for therapeutic intervention in neurodegenerative diseases. Many questions still need to be answered and future research in this field will result in exciting new discoveries that might impact other areas of biology.
Towards a Theoretical Model for Human Resource Management Information Systems , Government Policy and Organizational Performance : A Research Agenda

This study carries out a critical review of literature on human resource management information system, government policy and organization performance. The motivation for carrying out this literature review is presented and the point of contention is the application of human resource management information system, government policy and organizational performance. The objectives of carrying out this literature review include; to conceptualize the adoption of human resource management Information systems (HRMIS) and organization performance, to analyze the evolution of human resource information system (HRMIS) concept, to identify the theories upon which human resource information system (HRMIS) and organization performance are anchored upon, to critically review the empirical studies and identify the inherent gaps and to identify the factors that influences the adoption of HRMIS .The study reviews the origin of adoption of human resource management Information systems from both academic and management perspective. Factors influencing the adoption of human resource management systems, theoretical framework of human resource management systems whereby four theories namely diffusion theory, social capital theory, behavioral theory and resource based view theory have been discussed. An empirical review was done on thematic issues, methodology, data collection and data analysis. Various studies have been reviewed and analyzed to identify knowledge gaps. Conclusions were drawn and recommendations made based on the literature reviewed. A conceptual frame work alongside measures is proposed for studying human resource management information system, government policy and organizational performance and methodology for the study is also proposed.
A novel objective function for improved phoneme recognition using time delay neural networks

The authors present single- and multispeaker recognition results for the voiced stop consonants /b, d, g/ using time-delay neural networks (TDNN), a new objective function for training these networks, and a simple arbitration scheme for improved classification accuracy. With these enhancements a median 24% reduction in the number of misclassifications made by TDNNs trained with the traditional backpropagation objective function is achieved. This redundant results in /b, d, g/ recognition rates that consistently exceed 98% for TDNNs trained with individual speakers; it yields a 98.1% recognition rate for a TDNN trained with three male speakers.<<ETX>>
Array antenna integrated fan-out wafer level packaging (InFO-WLP) for millimeter wave system applications

Array antenna integrated with RF chip using InFO-WLP technology is proposed for millimeter wave system applications. Aperture-coupled patch antenna is designed on the fan-out molding compound (MC). The performance of single-element antenna is evaluated first and proved to have 5 dBi of gain. Meanwhile, the interconnect from chip to antenna feeding line is demonstrated to only have 0.7 dB loss, which can save 19 % PA output power compared with that of flip-chip package. Finally, the system performance of 4 × 4 antenna array integrated with RF chip on the InFO structure shows 14.7 dBi of array gain in a small form factor of 10 × 10 × 0.5 mm3.
Cyber-Crimes and their Impacts : A Review 1

In the current era of online processing, maximum of the information is online and prone to cyber threats. There are a huge number of cyber threats and their behavior is difficult to early understanding hence difficult to restrict in the early phases of the cyber attacks. Cyber attacks may have some motivation behind it or may be processed unknowingly. The attacks those are processed knowingly can be considered as the cyber crime and they have serious impacts over the society in the form of economical disrupt, psychological disorder, threat to National defense etc. Restriction of cyber crimes is dependent on proper analysis of their behavior and understanding of their impacts over various levels of society. Therefore, the current manuscript provides the understanding of cyber crimes and their impacts over society with the future trends of cyber crimes.
On the handling of continuous-valued attributes in decision tree generation

We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.
A Quantum Algorithm for Finding the Minimum

Let T [0..N −1] be an unsorted table of N items, each holding a value from an ordered set. For simplicity, assume that all values are distinct. The minimum searching problem is to find the index y such that T [y] is minimum. This clearly requires a linear number of probes on a classical probabilistic Turing machine. Here, we give a simple quantum algorithm which solves the problem using O( √ N) probes. The main subroutine is the quantum exponential searching algorithm of , which itself is a generalization of Grover’s recent quantum searching algorithm . Due to a general lower bound of , this is within a constant factor of the optimum.
Counting of cigarettes in cigarette packets using LabVIEW

The proposed work presents a technique for an automated count of cigarette in cigarette packets. Proposed work is based on application of image processing techniques in LabVIEW platform. An objective of the proposed work is to count the number of cigarettes in a packet. National Instrument's Smart camera is used to capture images of cigarette packets moving in packaging line and process the data to fulfill the above objective. The technique was subjected to offline testing on more than 50 number of cigarette packets and the results obtained are found to be satisfactory in all cases.
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection

The rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at a deep semantic level. Techniques for detecting such semantic redundancy at the document level are scarce. In this work we propose a deep Convolutional Neural Network (CNN) based model to classify a document as novel or redundant with respect to a set of relevant documents already seen by the system. The system is simple and does not require manual feature engineering. Our novel scheme encodes relevant and relative information from both source and target texts to generate an intermediate representation for which we coin the name Relative Document Vector (RDV). The proposed method outperforms the existing benchmark on two document-level novelty detection datasets by a margin of ∼5% in terms of accuracy. We further demonstrate the effectiveness of our approach on a standard paraphrase detection dataset where the paraphrased passages closely resembles semantically redundant documents.
Fostering sustainable behavior through community-based social marketing.

Psychology has a central role to play in speeding the transition to a sustainable future, because a central aspect of sustainability is widespread behavior change. To date, however, most programs promoting sustainable behavior have featured information-intensive campaigns that make little use of psychological knowledge. Community-based social marketing is an attractive alternative approach in which promoters identify the activity to be promoted and the barriers to this activity and then design a strategy to overcome these barriers, using psychological knowledge regarding behavior change. The strategy is piloted to test its effectiveness and later evaluated when it is implemented on a broader scale. Unlike many information-intensive campaigns, community-based social marketing has been shown to have a much greater probability of promoting sustainable behavior. Two case studies are provided to illustrate the approach and its possible results.
Topic scientific community in science: a combined perspective of scientific collaboration and topics

Scientific communities are clusters of researchers and play important roles in modern science. Studying different forms of scientific communities that either physically or virtually exist is a feasible way to disclose underlying mechanisms of science. From the perspective of complex networks, topology-based communities and topic-based communities reflect scientific collaboration and topical features of science respectively. However, the two features are not isolated but intertwined in scientific practice. This study proposes an approach to detect Topical Scientific Communities (TSCs) with both topology and topic features by applying machine learning techniques and network theory. As an example, the TSCs of the informetrics field are detected, and then the characteristics of these TSCs are analyzed. It is shown that collaboration patterns on the topic level can be revealed by analyzing the static network structure and dynamics of TSCs. Furthermore, cross-topic collaborations at multiple levels could be investigated through TSCs. In addition, TSCs can effectively organize researchers in terms of productivity. Future work will further explore and generalize characteristics of TSCs, and the applications of TSCs to other tasks of studying science.
Extending the RETE Algorithm for Event Management

A growingnumberof industrial applicationsuserule-basedprogramming. Frequently, theimplementation of theinferenceengineembeddedin theseapplicationsis basedontheRETEalgorithm.Someapplications supervisea flowof eventsin which time, throughtheoccurrencedatesof theevents,playsanimportantrole. Theseapplicationsneedto be able to recognizepatternsinvolving events.However theRETEalgorithm doesnotprovidesupportfor theexpressionof time-sensitivepatterns.Thispaperproposesan extensionof RETEthroughtheconceptsof time-stampedeventsandtemporal constraints betweenevents.Thisallows applicationsto write rulesthatprocessbothfactsandevents. Category, Track, and Topics Regular paper .  Topics: temporallanguagesand architectures, reasoningaboutactionsandchange,timeandnonmonotonism.
Learning from Simulated and Unsupervised Images through Adversarial Training

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulators output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a self-regularization term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.

What chiefly distinguishes cerebral cortex from other parts of the central nervous system is the great diversity of its cell types and inter-connexions. It would be astonishing if such a structure did not profoundly modify the response patterns of fibres coming into it. In the cat's visual cortex, the receptive field arrangements of single cells suggest that there is indeed a degree of complexity far exceeding anything yet seen at lower levels in the visual system. In a previous paper we described receptive fields of single cortical cells, observing responses to spots of light shone on one or both retinas (Hubel & Wiesel, 1959). In the present work this method is used to examine receptive fields of a more complex type (Part I) and to make additional observations on binocular interaction (Part II). This approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours. In the past, the technique of recording evoked slow waves has been used with great success in studies of functional anatomy. It was employed by Talbot & Marshall (1941) and by Thompson, Woolsey & Talbot (1950) for mapping out the visual cortex in the rabbit, cat, and monkey. Daniel & Whitteiidge (1959) have recently extended this work in the primate. Most of our present knowledge of retinotopic projections, binocular overlap, and the second visual area is based on these investigations. Yet the method of evoked potentials is valuable mainly for detecting behaviour common to large populations of neighbouring cells; it cannot differentiate functionally between areas of cortex smaller than about 1 mm2. To overcome this difficulty a method has in recent years been developed for studying cells separately or in small groups during long micro-electrode penetrations through nervous tissue. Responses are correlated with cell location by reconstructing the electrode tracks from histological material. These techniques have been applied to CAT VISUAL CORTEX 107 the somatic sensory cortex of the cat and monkey in a remarkable series of studies by Mountcastle (1957) and Powell & Mountcastle (1959). Their results show that the approach is a powerful one, capable of revealing systems of organization not hinted at by the known morphology. In Part III of the present paper we use this method in studying the functional architecture of the visual cortex. It helped us attempt to explain on anatomical …