nils-herrmann/clean_scirepeval huggingface dataset:
Selective Classification for Deep Neural Networks

Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, and almost 60% test coverage.
A Fast Index for Semistructured Data

Queries navigate semistructured data via path expressions, and can be accelerated using an index. Our solution encodes paths as strings, and inserts those strings into a special index that is highly optimized for long and complex keys. We describe the Index Fabric, an indexing structure that provides the efficiency and flexibility we need. We discuss how "raw paths" are used to optimize ad hoc queries over semistructured data, and how "refined paths" optimize specific access paths. Although we can use knowledge about the queries and structure of the data to create refined paths, no such knowledge is needed for raw paths. A performance study shows that our techniques, when implemented on top of a commercial relational database system, outperform the more traditional approach of using the commercial system’s indexing mechanisms to query the XML.
FUNDAMENTALS OF EEG MEASUREMENT

Electroencephalographic measurements are commonly used in medical and research areas. This review article presents an introduction into EEG measurement. Its purpose is to help with orientation in EEG field and with building basic knowledge for performing EEG recordings. The article is divided into two parts. In the first part, background of the subject, a brief historical overview, and some EEG related research areas are given. The second part explains EEG recording.
Study on flip chip assembly of high density micro-LED array

Flip chip assembly technology is an attractive solution for high I/O density and fine-pitch microelectronics packaging. Recently, high efficient GaN-based light-emitting diodes (LEDs) have undergone a rapid development and flip chip bonding has been widely applied to fabricate high-brightness GaN micro-LED arrays . The flip chip GaN LED has some advantages over the traditional top-emission LED, including improved current spreading, higher light extraction efficiency, better thermal dissipation capability and the potential of further optical component integration [2, 3]. With the advantages of flip chip assembly, micro-LED (μLED) arrays with high I/O density can be performed with improved luminous efficiency than conventional p-side-up micro-LED arrays and are suitable for many potential applications, such as micro-displays, bio-photonics and visible light communications (VLC), etc. In particular, μLED array based selif-emissive micro-display has the promising to achieve high brightness and contrast, reliability, long-life and compactness, which conventional micro-displays like LCD, OLED, etc, cannot compete with. In this study, GaN micro-LED array device with flip chip assembly package process was presented. The bonding quality of flip chip high density micro-LED array is tested by daisy chain test. The p-n junction tests of the devices are measured for electrical characteristics. The illumination condition of each micro-diode pixel was examined under a forward bias. Failure mode analysis was performed using cross sectioning and scanning electron microscopy (SEM). Finally, the fully packaged micro-LED array device is demonstrated as a prototype of dice projector system.
Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection

Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal models. Second, the classifiers are trained using very large datasets which may be time consuming to obtain in practice. When the size of training dataset is small, the performance of a single BN classifier is significantly reduced due to its inability to represent the whole probability distribution. To alleviate these problems, we build a Bayesian classifier by Bayesian Model Averaging(BMA) over the k-best BN classifiers, called Bayesian Network Model Averaging (BNMA) classifier. We train and evaluate BNMA classifier on the NSL-KDD dataset, which is less redundant, thus more judicial than the commonly used KDD Cup 99 dataset. We show that the BNMA classifier performs significantly better in terms of detection accuracy than the Naive Bayes classifier and the BN classifier built with heuristic method. We also show that the BNMA classifier trained using a smaller dataset outperforms two other classifiers trained using a larger dataset. This also implies that the BNMA is beneficial in accelerating the detection process due to its less dependance on the potentially prolonged process of collecting large training datasets.
Detecting Depression Severity from Vocal Prosody

To investigate the relation between vocal prosody and change in depression severity over time, 57 participants from a clinical trial for treatment of depression were evaluated at seven-week intervals using a semistructured clinical interview for depression severity (Hamilton Rating Scale for Depression (HRSD)). All participants met criteria for major depressive disorder (MDD) at week one. Using both perceptual judgments by naive listeners and quantitative analyses of vocal timing and fundamental frequency, three hypotheses were tested: 1) Naive listeners can perceive the severity of depression from vocal recordings of depressed participants and interviewers. 2) Quantitative features of vocal prosody in depressed participants reveal change in symptom severity over the course of depression. 3) Interpersonal effects occur as well; such that vocal prosody in interviewers shows corresponding effects. These hypotheses were strongly supported. Together, participants' and interviewers' vocal prosody accounted for about 60 percent of variation in depression scores, and detected ordinal range of depression severity (low, mild, and moderate-to-severe) in 69 percent of cases (kappa = 0.53). These findings suggest that analysis of vocal prosody could be a powerful tool to assist in depression screening and monitoring over the course of depressive disorder and recovery.
Control scheme and networked control architecture for the Berkeley lower extremity exoskeleton (BLEEX)

The Berkeley lower extremity exoskeleton (BLEEX) is a load-carrying and energetically autonomous human exoskeleton that, in this first generation prototype, carries up to a 34 kg (75 Ib) payload for the pilot and allows the pilot to walk at up to 1.3 m/s (2.9 mph). This article focuses on the human-in-the-loop control scheme and the novel ring-based networked control architecture (ExoNET) that together enable BLEEX to support payload while safely moving in concert with the human pilot. The BLEEX sensitivity amplification control algorithm proposed here increases the closed loop system sensitivity to its wearer's forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The tradeoffs between not having sensors to measure human variables, the need for dynamic model accuracy, and robustness to parameter uncertainty are described. ExoNET provides the physical network on which the BLEEX control algorithm runs. The ExoNET control network guarantees strict determinism, optimized data transfer for small data sizes, and flexibility in configuration. Its features and application on BLEEX are described
Breaking and entering through the silicon

As the surplus market of failure analysis equipment continues to grow, the cost of performing invasive IC analysis continues to diminish. Hardware vendors in high-security applications utilize security by obscurity to implement layers of protection on their devices. High-security applications must assume that the attacker is skillful, well-equipped and well-funded. Modern security ICs are designed to make readout of decrypted data and changes to security configuration of the device impossible. Countermeasures such as meshes and attack sensors thwart many state of the art attacks. Because of the perceived difficulty and lack of publicly known attacks, the IC backside has largely been ignored by the security community. However, the backside is currently the weakest link in modern ICs because no devices currently on the market are protected against fully-invasive attacks through the IC backside. Fully-invasive backside attacks circumvent all known countermeasures utilized by modern implementations. In this work, we demonstrate the first two practical fully-invasive attacks against the IC backside. Our first attack is fully-invasive backside microprobing. Using this attack we were able to capture decrypted data directly from the data bus of the target IC's CPU core. We also present a fully invasive backside circuit edit. With this attack we were able to set security and configuration fuses of the device to arbitrary values.
Formation Control of Leader–Follower Mobile Robots’ Systems Using Model Predictive Control Based on Neural-Dynamic Optimization

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for controlling leader-follower mobile robots formation. Consider obstacles in the environments, a control strategy is proposed for the formations which includes separation-bearing-orientation scheme (SBOS) for regular leader-follower formation and separation-distance scheme (SDS) for obstacle avoidance. During the formation motion, the leader robot shall track a desired trajectory and the desire leader-follower relationship can be maintained through SBOS method; meanwhile, the followers can avoid the collision by applying the SDS. The formation-error kinematics of both SBOS and SDS are derived and a constrained quadratic programming (QP) can be obtained by transforming the MPC method. Then, over a finite-receding horizon, the QP problem can be solved by utilizing the primal-dual neural network (PDNN) with parallel capability. The computation complexity can be greatly reduced by the implemented neural-dynamic optimization. Compared with other existing formation control approaches, the developed solution in this paper is rooted in NMPC techniques with input constraints and the novel QP problem formulation. Finally, experimental studies of the proposed formation control approach have been performed on several mobile robots to verify the effectiveness.
Job Satisfaction and Employees ’ Turnover Intentions in total Nigeria plc . in Lagos State

This paper examines job satisfaction and employees’ turnover intentions in Total Nigeria PLC in Lagos State. The paper highlights and defines basic concepts of job satisfaction and employees’ turnover intention. It specifically considered satisfaction with pay, nature of work and supervision as the three facets of job satisfaction that affect employee turnover intention. To achieve this objective, authors adopted a survey method by administration of questionnaires, conducting interview and by reviewing archival documents as well as review of relevant journals and textbooks in this field of learning as means of data collection. Four (4) major hypotheses were derived from literature and respective null hypotheses tested at .05 level of significance It was found that specifically job satisfaction reduces employees’ turnover intention and that Total Nigeria PLC adopts standard pay structure, conducive nature of work and efficient supervision not only as strategies to reduce employees’ turnover but also as the company retention strategy.
Foundations of SPARQL query optimization

We study fundamental aspects related to the efficient processing of the SPARQL query language for RDF, proposed by the W3C to encode machine-readable information in the Semantic Web. Our key contributions are (i) a complete complexity analysis for all operator fragments of the SPARQL query language, which -- as a central result -- shows that the SPARQL operator Optional alone is responsible for the PSpace-completeness of the evaluation problem, (ii) a study of equivalences over SPARQL algebra, including both rewriting rules like filter and projection pushing that are well-known from relational algebra optimization as well as SPARQL-specific rewriting schemes, and (iii) an approach to the semantic optimization of SPARQL queries, built on top of the classical chase algorithm. While studied in the context of a theoretically motivated set semantics, almost all results carry over to the official, bag-based semantics and therefore are of immediate practical relevance.
Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening

Reliable verification of image quality of retinal screening images is a prerequisite for the development of automatic screening systems for diabetic retinopathy. A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis. The system is based on the assumption that an image of sufficient quality should contain particular image structures according to a certain pre-defined distribution. We cluster filterbank response vectors to obtain a compact representation of the image structures found within an image. Using this compact representation together with raw histograms of the R, G, and B color planes, a statistical classifier is trained to distinguish normal from low quality images. The presented system does not require any previous segmentation of the image in contrast with previous work. The system was evaluated on a large, representative set of 1000 images obtained in a screening program. The proposed method, using different feature sets and classifiers, was compared with the ratings of a second human observer. The best system, based on a Support Vector Machine, has performance close to optimal with an area under the ROC curve of 0.9968.
Transmission of IPv6 Packets over IEEE 802.15.4 Networks

This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards" (STD 1) for the standardization state and status of this protocol. Distribution of this memo is unlimited.  This document describes the frame format for transmission of IPv6 packets and the method of forming IPv6 link-local addresses and statelessly autoconfigured addresses on IEEE 802.15.4 networks. Additional specifications include a simple header compression scheme using shared context and provisions for packet delivery in IEEE 802.15.4 meshes.
Electric powered wheelchairs

The purpose of this review is to convey the depth and breadth of the research conducted on electric-powered wheelchair (EPW) control technology as well as provide insights into future directions. We review the concepts and previous work on velocity control, traction control, suspension control, stability control, stair-climbing wheelchairs, and wheelchair navigation. The information gathered in this study is intended to promote awareness of the status of contemporary powered wheelchair control technology and increase the functional mobility of people who use EPWs.
Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance

The most common iris biometric algorithm represents the texture of an iris using a binary iris code. Not all bits in an iris code are equally consistent. A bit is deemed fragile if its value changes across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by masking these fragile bits. Rather than ignoring fragile bits completely, we consider what beneficial information can be obtained from the fragile bits. We find that the locations of fragile bits tend to be consistent across different iris codes of the same eye. We present a metric, called the fragile bit distance, which quantitatively measures the coincidence of the fragile bit patterns in two iris codes. We find that score fusion of fragile bit distance and Hamming distance works better for recognition than Hamming distance alone. To our knowledge, this is the first and only work to use the coincidence of fragile bit locations to improve the accuracy of matches.
A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising

There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher's revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser's purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed contracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.
Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition

We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive matches, where the obtained recall rate almost doubles the best reported result to date. We discuss the various components and innovations of our system that enable this significant performance gap. These components include extensive utilization of an accurate 3D reconstructed shape model dealing with challenges arising from pose and illumination. In addition, discriminative models based on billions of faces are used in order to overcome aging and facial expression as well as low light and overexposure. Finally, we identify a challenging set of identification queries that might provide useful focus for future research. 1 Benchmark and results The LFW benchmark [6] has become the de-facto standard testbed for unconstrained face recognition with over 100 citations in the face recognition literature since its debut 3 years ago. Extensive work [15, 14, 13, 5, 7, 4, 10, 3, 8, 9, 11, 16] has been invested in improving the recognition score which has been considerably increased since the first non-trivial result of 72% accuracy. We employ face.com’s r2011b face recognition engine to the LFW benchmark without any dataset specific pre-tuning. The obtained mean accuracy is 91.3% ± 0.3, achieved on the test set (view 2) under the unrestricted LFW protocol. Figure 1 (a) presents the ROC curve obtained in comparison to previous results. Remarkably, much of the obtained improvement is achieved at the conservative performance range, i.e., at low False Acceptance Rates (FAR). 1face.com has a public API service  which currently employs a previous version of the engine. 1 ar X iv :1 10 8. 11 22 v1 [ cs .C V ] 4 A ug 2 01 1
Human Effort and Machine Learnability in Computer Aided Translation

Analyses of computer aided translation typically focus on either frontend interfaces and human effort, or backend translation and machine learnability of corrections. However, this distinction is artificial in practice since the frontend and backend must work in concert. We present the first holistic, quantitative evaluation of these issues by contrasting two assistive modes: postediting and interactive machine translation (MT). We describe a new translator interface, extensive modifications to a phrasebased MT system, and a novel objective function for re-tuning to human corrections. Evaluation with professional bilingual translators shows that post-edit is faster than interactive at the cost of translation quality for French-English and EnglishGerman. However, re-tuning the MT system to interactive output leads to larger, statistically significant reductions in HTER versus re-tuning to post-edit. Analysis shows that tuning directly to HTER results in fine-grained corrections to subsequent machine output.
Detecting near-duplicates for web crawling

Near-duplicate web documents are abundant. Two such documents differ from each other in a very small portion that displays advertisements, for example. Such differences are irrelevant for web search. So the quality of a web crawler increases if it can assess whether a newly crawled web page is a near-duplicate of a previously crawled web page or not. In the course of developing a near-duplicate detection system for a multi-billion page repository, we make two research contributions. First, we demonstrate that Charikar's fingerprinting technique is appropriate for this goal. Second, we present an algorithmic technique for identifying existing f-bit fingerprints that differ from a given fingerprint in at most k bit-positions, for small k. Our technique is useful for both online queries (single fingerprints) and all batch queries (multiple fingerprints). Experimental evaluation over real data confirms the practicality of our design.
Improving agile requirements: the Quality User Story framework and tool

User stories are a widely adopted requirements notation in agile development. Yet, user stories are too often poorly written in practice and exhibit inherent quality defects. Triggered by this observation, we propose the Quality User Story (QUS) framework, a set of 13 quality criteria that user story writers should strive to conform to. Based on QUS, we present the Automatic Quality User Story Artisan (AQUSA) software tool. Relying on natural language processing (NLP) techniques, AQUSA detects quality defects and suggest possible remedies. We describe the architecture of AQUSA, its implementation, and we report on an evaluation that analyzes 1023 user stories obtained from 18 software companies. Our tool does not yet reach the ambitious 100 % recall that Daniel Berry and colleagues require NLP tools for RE to achieve. However, we obtain promising results and we identify some improvements that will substantially improve recall and precision.