nils-herrmann/clean_scirepeval huggingface dataset:
Research and improvement on ID3 algorithm in intrusion detection system

ID3 algorithm was a classic classification of data mining. It always selected the attribute with many values. The attribute with many values wasn't the correct one, and it always created wrong classification. In the application of intrusion detection system, it would created fault alarm and omission alarm. To this fault, an improved decision tree algorithm was proposed. Though improvement of information gain formula, the correct attribute would be got. The decision tree was created after the data collected classified correctly. The tree would be not high and has a few of branches. The rule set would be got based on the decision tree. Experimental results showed the effectiveness of the algorithm, false alarm rate and omission rate decreased, increasing the detection rate and reducing the space consumption.
Certificate Validation Through Public Ledgers and Blockchains

Public key infrastructures (PKIs) are of crucial importance for the life of online services relying on certificate-based authentication, like e-commerce, e-government, online banking, as well as e-mail, social networking, cloud services and many others. One of the main points of failure (POFs) of modern PKIs concerns reliability and security of certificate revocation lists (CRLs), that must be available and authentic any time a certificate is used. Classically, the CRL for a set of certificates is maintained by the same (and sole) certification authority (CA) that issued the certificates, and this introduces a single POF in the system. We address this issue by proposing a solution in which multiple CAs share a public, decentralized and robust ledger where CRLs are collected. For this purpose, we consider the model of public ledgers based on blockchains, introduced for the use in cryptocurrencies, that is becoming a widespread solution for many online applications with stringent security and reliability requirements.
Fast QR Code Detection in Arbitrarily Acquired Images

The detection of QR codes, a type of 2D barcode, as described in the literature consists merely in the determination of the boundaries of the symbol region in images obtained with the specific intent of highlighting the symbol. However, many important applications such as those related with accessibility technologies or robotics, depends on first detecting the presence of a barcode in an environment. We employ Viola-Jones rapid object detection framework to address the problem of finding QR codes in arbitrarily acquired images. This framework provides an efficient way to focus the detection process in promising regions of the image and a very fast feature calculation approach for pattern classification. An extensive study of variations in the parameters of the framework for detecting finder patterns, present in three corners of every QR code, was carried out. Detection accuracy superior to 90%, with controlled number of false positives, is achieved. We also propose a post-processing algorithm that aggregates the results of the first step and decides if the detected finder patterns are part of QR code symbols. This two-step processing is done in real time.
A robust multiple watermarking scheme in the Discrete Wavelet Transform domain

Because of the transition from analog to digital technologies, content owners are seeking technologies for the protection of copyrighted multimedia content. Encryption and watermarking are two major tools that can be used to prevent unauthorized consumption and duplication. In this paper, we generalize an idea in a recent paper that embeds a binary pattern in the form of a binary image in the LL and HH bands at the second level of Discrete Wavelet Transform (DWT) decomposition. Our generalization includes all four bands (LL, HL, LH, and HH), and a comparison of embedding a watermark at first and second level decompositions. We tested the proposed algorithm against fifteen attacks. Embedding the watermark in lower frequencies is robust to a group of attacks, and embedding the watermark in higher frequencies is robust to another set of attacks. Only for rewatermarking and collusion attacks, the watermarks extracted from all four bands are identical. Our experiments indicate that first level decomposition appear advantageous for two reasons: The area for watermark embedding is maximized, and the extracted watermarks are more textured with better visual quality.
Computational State Space Models for Activity and Intention Recognition. A Feasibility Study

Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity.



A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance.



The symbolic domain model was found to have more than 10(8) states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure.



Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also show that research on CSSMs needs to consider sufficiently complex domains in order to understand the effects of design decisions such as choice of heuristics or inference procedure on performance.
Mother-Infant Face-to-Face Interaction : Influence is Bidirectional and Unrelated to Periodic Cycles in Either Partner ' s Behavior

During mother-infant face-to-face interactions, bidirectional influence could be achieved through either the entraining of periodic cycles in the behavior of each partner or through the stochastic organization of behaviors. To determine whether and how bidirectional influence occurs, we used both timeand frequency-domain techniques to study the interactions of 54 mother-infant pairs, 18 each at 3, 6, and 9 months of age. Behavioral descriptors for each mother and infant were scaled to reflect levels of affective involvement during each second oftbe interaction. Periodic cycles were found in infants' expressive behavior only at 3 months and not in mothers' behavior. Nonperiodic cycles, which were found in some mothers' and infants' behavior at each age, were more common. At no age was the occurrence of cycles in mothers' or infants' behavior related to the achievement of bidirectional influence. Similar proportions of mothers and infants were responsive to momentto-moment changes in the other's behavior, except at 6 months when the proportion of mothers was higher. Bidirectional influence was brought about by the stochastic organization of behaviors rather than through the mutual entraining of periodic cycles.
Normally-off high-voltage p-GaN gate GaN HFET with carbon-doped buffer

Normally-off GaN transistors for power applications in p-type GaN gate technology with a modified carbon-doped GaN buffer are presented. A combination of an AlGaN back-barrier with the carbon-doped buffer prevents early off-state punch-through. Simultaneously, the on-state resistance could be kept low and the threshold voltage with 1.1 V high enough for secure normally-off operation. 1000 V breakdown strength has been obtained for devices with 6 μm gate-drain spacing. The resulting breakdown scaling slope is 170 V/μm gate-drain distance. The on-state resistance is 7.4 Ωmm. The resulting VBr-to-RONA ratio (1000 V, 0.62 mΩcm2) is beyond so far reported ratios for normally-off GaN transistors. Modifications of the p-type GaN layer have shown to additionally increase the threshold voltage by 0.4 V without paying a price in the on-state resistance of the device.
Bellman Gradient Iteration for Inverse Reinforcement Learning

This paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly handle different types of actions with two approximations of the Bellman Optimality Equation, and a Bellman Gradient Iteration method to compute the gradient of the Qvalue with respect to the reward function. These methods allow us to build a differentiable relation between the Q-value and the reward function and learn an approximately optimal reward function with gradient methods. We test the proposed method in two simulated environments by evaluating the accuracy of different approximations and comparing the proposed method with existing solutions. The results show that even with a linear reward function, the proposed method has a comparable accuracy with the state-of-the-art method adopting a non-linear reward function, and the proposed method is more flexible because it is defined on observed actions instead of trajectories
Virtual Petz: A Hybrid Approach to Creating Autonomous, Lifelike Dogz and Catz

1.  WC have developed a series of lifelike computer characters called Virtual Petz. These are autonomous agents with real-time layered 3D animation and sound. Using a mouse the user moves a hand-shaped cursor to directly touch, pet, and pick up the characters, as well as use toys and objects. Virtual Petz grow up over time on the user’s PC computer desktop, and strive to be the user’s friends and companions. They have evolving social relationships with the user and each other. To implement these agents we have invented hybrid techniques that draw from cartoons, improvisational drama, AI and video games.
Planar Quasi-Isotropic Magnetic Dipole Antenna Using Fractional-Order Circular Sector Cavity Resonant Mode

The radiation behavior of the fractional-order, resonant mode within a circular sector cavity radiator is revealed at first, and then, it is employed to design a novel, planar quasi-isotropic magnetic dipole antenna. A set of closed-form formulas is derived and employed to determine the key parameters of the proposed antenna. The resultant circular sector magnetic dipole antenna operates at its dominant TM(2/3), 1 mode. It is numerically verified and experimentally validated at the 2.45-GHz band. It is seen that the antenna exhibits a good non-uniformity of less than 5.7 dB within the three principal planes, and an average radiation efficiency up to 82% within its impedance bandwidth from 2.4 to 2.5 GHz (for reflection coefficient smaller than −10 dB). Good agreement between the theoretical, simulated, and measured results has evidently verified the proposed antenna design approach.
A systematic review and taxonomy of explanations in decision support and recommender systems

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.
Deterministic Process Groups in dOS

Current multiprocessor systems execute parallel and concurrent software nondeterministically: even when given precisely the same input, two executions of the same program may produce different output. This severely complicates debugging, testing, and automatic replication for fault-tolerance. Previous efforts to address this issue have focused primarily on record and replay, but making execution actually deterministic would address the problem at the root. Our goals in this work are twofold: (1) to provide fully deterministic execution of arbitrary, unmodified, multithreaded programs as an OS service; and (2) to make all sources of intentional nondeterminism, such as network I/O, be explicit and controllable. To this end we propose a new OS abstraction, the Deterministic Process Group (DPG). All communication between threads and processes internal to a DPG happens deterministically, including implicit communication via sharedmemory accesses, as well as communication via OS channels such as pipes, signals, and the filesystem. To deal with fundamentally nondeterministic external events, our abstraction includes the shim layer, a programmable interface that interposes on all interaction between a DPG and the external world, making determinism useful even for reactive applications. We implemented the DPG abstraction as an extension to Linux and demonstrate its benefits with three use cases: plain deterministic execution; replicated execution; and record and replay by logging just external input. We evaluated our implementation on both parallel and reactive workloads, including Apache, Chromium, and PARSEC.
Why Business Intelligence?: Significance of Business Intelligence Tools and Integrating BI Governance with Corporate Governance

Business Intelligence (BI) is one of the fastest growing software sector and software vendors are rapidly developing multiple BI tools to support the growing data analysis needs of organisations. In order to be sustainable in a briskly changing turbulent environment, organisations need to have access to information about their operational performance. BI tools play a vital role in supporting the decision makers at different organisational levels. As these tools are becoming critical in decision making, it has become not only an information technology concern but also a management concern. Without proper governance it would be impractical to achieve the value that BI tools offer. Adopting a BI governance framework in organisations will lead to common principles and clear ownership over information. Additionally, appropriate alignment between corporate governance and BI governance can yield more benefits. This paper provides an insight into the importance and value of BI tools. Key functionalities of BI tools have also been highlighted. Different challenges in gaining true value from BI tools have been examined. Four phases of developing a BI governance framework have been illustrated. The alignment between BI governance and corporate governance has also been explored with a recommended model. Exploratory analysis of two organisations (Premier Healthcare Alliance & BellSouth Telecommunications) to identify how they have utilised BI tools and adopted BI governance has been briefly carried out. The paper posits that if the steps for developing a BI framework are adopted by organisations and the BI framework is aligned with the corporate framework, BI deployment and usage will be successful with reduced risk levels. Why Business Intelligence?
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration

As aggregators, online news portals face great challenges in continuously selecting a pool of candidate articles to be shown to their users. Typically, those candidate articles are recommended manually by platform editors from a much larger pool of articles aggregated from multiple sources. Such a hand-pick process is labor intensive and time-consuming. In this paper, we study the editor article selection behavior and propose a learning by demonstration system to automatically select a subset of articles from the large pool. Our data analysis shows that (i) editors' selection criteria are non-explicit, which are less based only on the keywords or topics, but more depend on the quality and attractiveness of the writing from the candidate article, which is hard to capture based on traditional bag-of-words article representation. And (ii) editors' article selection behaviors are dynamic: articles with different data distribution come into the pool everyday and the editors' preference varies, which are driven by some underlying periodic or occasional patterns. To address such problems, we propose a meta-attention model across multiple deep neural nets to (i) automatically catch the editors' underlying selection criteria via the automatic representation learning of each article and its interaction with the meta data and (ii) adaptively capture the change of such criteria via a hybrid attention model. The attention model strategically incorporates multiple prediction models, which are trained in previous days. The system has been deployed in a commercial article feed platform. A 9-day A/B testing has demonstrated the consistent superiority of our proposed model over several strong baselines.
The BellKor solution to the Netflix Prize

Our final solution (RMSE=0.8712) consists of blending 107 individual results. Since many of these results are close variants, we first describe the main approaches behind them. Then, we will move to describing each individual result. The core components of the solution are published in our ICDM'2007 paper  (or, KDD-Cup'2007 paper ), and also in the earlier KDD'2007 paper . We assume that the reader is familiar with these works and our terminology there. A movie-oriented k-NN approach was thoroughly described in our KDD-Cup'2007 paper [kNN]. We apply it as a post-processor for most other models. Interestingly, it was most effective when applied on residuals of RBMs , thereby driving the Quiz RMSE from 0.9093 to 0.8888. An earlier k-NN approach was described in the KDD'2007 paper (, Sec. 3) [Slow-kNN]. It appears that this earlier approach can achieve slightly more accurate results than the newer one, at the expense of a significant increase in running time. Consequently, we dropped the older approach, though some results involving it survive within the final blend. We also tried more naïve k-NN models, where interpolation weights are based on pairwise similarities between movies (see , Sec. 2.2). Specifically, we based weights on corr 2 /(1-corr 2) [Corr-kNN], or on mse-10 [MSE-kNN]. Here, corr is the Pearson correlation coefficient between the two respective movies, and mse is the mean squared distance between two movies (see definition of s ij in Sec. 4.1 of ). We also tried taking the interpolation weights as the "support-based similarities", which will be defined shortly [Supp-kNN]. Other variants that we tried for computing the interpolation coefficients are: (1) using our KDD-Cup'2007  method on a binary user-movie matrix, which replaces every rating with " 1 " , and sets non-rated user-movie pairs to " 0 " [Bin-kNN]. (2) Taking results of factorization, and regressing the factors associated with the target movie on the factors associated with its neighbors. Then, the resulting regression coefficients are used as interpolation weights [Fctr-kNN]. As explained in our papers, we also tried user-oriented k-NN approaches. Either in a profound way (see: , Sec. 4.3; , Sec. 5) [User-kNN], or by just taking weights as pairwise similarities among users [User-MSE-kNN], which is the user-oriented parallel of the aforementioned [MSE-kNN]. Prior to computing interpolation weights, one has to choose the set of neighbors. We find the most similar neighbors based on an appropriate similarity measure. In …
Essentials of electronic testing for digital, memory, and mixed-signal VLSI circuits [Book Review]

The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. M. Bushnell, V.D. Agrawal Essentials of Electronic Testing for Digital, Memory and MixedSignal VLSI Circuits
Self-Calibration from Multiple Views with a Rotating Camera

A new practical method is given for the self-calibration of a camera. In this method, at least three images are taken from the same point in space with different orientations of the camera and calibration is computed from an analysis of point matches between the images. The method requires no knowledge of the orientations of the camera. Calibration is based on the image correspondences only. This method differs fundamentally from previous results by Maybank and Faugeras on selfcalibration using the epipolar structure of image pairs. In the method of this paper, there is no epipolar structure since all images are taken from the same point in space. Since the images are all taken from the same point in space, determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur. The calibration method is evaluated on several sets of synthetic and real image data.
Serious Vascular Complications after Nonsurgical Rhinoplasty: A Case Report.

There has been an increased global demand for dermal filler injections in recent years. Although hyaluronic acid-based dermal fillers generally have a good safety profile, serious vascular complications have been reported. Here we present a typical case of skin necrosis following a nonsurgical rhinoplasty using hyaluronic acid filler. Despite various rescuing managements, unsightly superficial scars were left. It is critical for plastic surgeons and dermatologists to be familiar with the vascular anatomy and the staging of vascular complications. Any patients suspected to experience a vascular complication should receive early management under close monitoring. Meanwhile, the potentially devastating outcome caused by illegal practice calls for stricter regulations and law enforcement.