nils-herrmann/clean_scirepeval huggingface dataset:
Strengthening the empirical analysis of the relationship between Linus' Law and software security

Open source software is often considered to be secure because large developer communities can be leveraged to find and fix security vulnerabilities. Eric Raymond states Linus' Law as "many eyes make all bugs shallow", reasoning that a diverse set of perspectives improves the quality of a software product. However, at what point does the multitude of developers become "too many cooks in the kitchen", causing the system's security to suffer as a result? In a previous study, we quantified Linus' Law and "too many cooks in the kitchen" with developer activity metrics and found a statistical association between these metrics and security vulnerabilities in the Linux kernel. In the replication study reported in this paper, we performed our analysis on two additional projects: the PHP programming language and the Wireshark network protocol analyzer. We also updated our Linux kernel case study with 18 additional months of newly-discovered vulnerabilities. In all three case studies, files changed by six developers or more were at least four times more likely to have a vulnerability than files changed by fewer than six developers. Furthermore, we found that our predictive models improved on average when combining data from multiple projects, indicating that models can be transferred from one project to another.
Dynamic Input Structure and Network Assembly for Few-Shot Learning

The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception. While methods have succeeded with large amounts of training data, research has been underway in how to accomplish similar performance with fewer examples, known as one-shot or more generally few-shot learning. This technique has been shown to have promising performance, but in practice requires fixed-size inputs making it impractical for production systems where class sizes can vary. This impedes training and the final utility of few-shot learning systems. This paper describes an approach to constructing and training a network that can handle arbitrary example sizes dynamically as the system is used.
Verification of Arithmetic Circuits with Binary Moment Diagrams

Binary Moment Diagrams (BMDs) provide a canonical representations for linear functions similar to the way Binary Decision Diagrams (BDDs) represent Boolean functions. Within the class of linear functions, we can embed arbitrary functions from Boolean variables to integer values. BMDs can thus model the functionality of data path circuits operating over word-level data. Many important functions, including integermultiplication, that cannot be represented efficiently at the bit level with BDDs have simple representations at the word level with BMDs. Furthermore, BMDs can represent Boolean functions with around the same complexity as BDDs. We propose a hierarchical approach to verifying arithmetic circuits, where componentmodules are first shownto implement their word-level specifications. The overall circuit functionality is then verified by composing the component functions and comparing the result to the word-level circuit specification. Multipliers with word sizes of up to 256 bits have been verified by this technique.
Semi-Autonomous Data Enrichment and Optimisation for Intelligent Speech Analysis

Intelligent Speech Analysis (ISA) plays an essential role in smart conversational agent systems that aim to enable natural, intuitive, and friendly human computer interaction. It includes not only the long-term developed Automatic Speech Recognition (ASR), but also the young field of Computational Paralinguistics, which has attracted increasing attention in recent years. In real-world applications, however, several challenging issues surrounding data quantity and quality arise. For example, predefined databases for most paralinguistic tasks are normally quite small and few in number, which are insufficient for building a robust model. A distributed structure could be useful for data collection, but original feature sets are always too large to meet the physical transmission requirements, for example, bandwidth limitation. Furthermore, in a hands-free application scenario, reverberation severely distorts speech signals, which results in performance degradation of recognisers. To address these issues, this thesis proposes and analyses semi-autonomous data enrichment and optimisation approaches. More precisely, for the representative paralinguistic task of speech emotion recognition, both labelled and unlabelled data from heterogeneous resources are exploited by methods of data pooling, data selection, confidence-based semi-supervised learning, active learning, as well as cooperative learning. As a result, the manual work for data annotation is greatly reduced. With the advance of networks and information technologies, this thesis extends the traditional ISA system into a modern distributed paradigm, in which Split Vector Quantisation is employed for feature compression. Moreover, for distant-talk ASR, Long Short-Term Memory (LSTM) recurrent neural networks, which are known to be well-suited to context-sensitive pattern recognition, are evaluated to mitigate reverberation. The experimental results demonstrate that the proposed LSTM-based feature enhancement frameworks prevail over the current state-of-the-art methods.
ERICA: Interaction Mining Mobile Apps

Design plays an important role in adoption of apps. App design, however, is a complex process with multiple design activities. To enable data-driven app design applications, we present interaction mining -- capturing both static (UI layouts, visual details) and dynamic (user flows, motion details) components of an app's design. We present ERICA, a system that takes a scalable, human-computer approach to interaction mining existing Android apps without the need to modify them in any way. As users interact with apps through ERICA, it detects UI changes, seamlessly records multiple data-streams in the background, and unifies them into a user interaction trace. Using ERICA we collected interaction traces from over a thousand popular Android apps. Leveraging this trace data, we built machine learning classifiers to detect elements and layouts indicative of 23 common user flows. User flows are an important component of UX design and consists of a sequence of UI states that represent semantically meaningful tasks such as searching or composing. With these classifiers, we identified and indexed more than 3000 flow examples, and released the largest online search engine of user flows in Android apps.
Sentiment analysis of movie reviews on discussion boards using a linguistic approach

We propose a linguistic approach for sentiment analysis of message posts on discussion boards. A sentence often contains independent clauses which can represent different opinions on the multiple aspects of a target object. Therefore, the proposed system provides clause-level sentiment analysis of opinionated texts. For each sentence in a message post, it generates a dependency tree, and splits the sentence into clauses. Then it determines the contextual sentiment score for each clause utilizing grammatical dependencies of words and the prior sentiment scores of the words derived from SentiWordNet and domain specific lexicons. Negation is also delicately handled in this study, for instance, the term "not superb" is assigned a lower negative sentiment score than the term "not good". We have experimented with a dataset of movie review sentences, and the experimental results show the effectiveness of the proposed approach.
Classifier based graph construction for video segmentation

Video segmentation has become an important and active research area with a large diversity of proposed approaches. Graph-based methods, enabling top-performance on recent benchmarks, consist of three essential components: 1. powerful features account for object appearance and motion similarities; 2. spatio-temporal neighborhoods of pixels or superpixels (the graph edges) are modeled using a combination of those features; 3. video segmentation is formulated as a graph partitioning problem. While a wide variety of features have been explored and various graph partition algorithms have been proposed, there is surprisingly little research on how to construct a graph to obtain the best video segmentation performance. This is the focus of our paper. We propose to combine features by means of a classifier, use calibrated classifier outputs as edge weights and define the graph topology by edge selection. By learning the graph (without changes to the graph partitioning method), we improve the results of the best performing video segmentation algorithm by 6% on the challenging VSB100 benchmark, while reducing its runtime by 55%, as the learnt graph is much sparser.
Microstrip to waveguide transition dedicated to wireless millimeter-wave applications

This paper propose a novel millimeter-wave microstrip (MSL) to WR 12 standard rectangular waveguide transition. These dissimilar structures are interconnected via a trapezoidal substrate integrated waveguide (TSIW). The MSL and the TSIW are integrated on the same ceramic substrate of 9.9 of permittivity and 125 of thickness. The MSL has been first transformed into an air-filled rectangular waveguide, and then a horn transformer has been used to match the WR12 waveguide. The central frequency of operation is 61 GHz and dedicated to V-band wireless communications applications. The S parameters measurements of the back-to-back connected transitions show an insertion loss less than 1.5 dB and a return loss better than -10 dB over a 5 GHz bandwidth from 60 to 65 GHz. The high performance and the compact size, enable the transition to be employed in a number of millimeter-wave applications.
The distributed human neural system for face perception

Face perception, perhaps the most highly developed visual skill in humans, is mediated by a distributed neural system in humans that is comprised of multiple, bilateral regions. We propose a model for the organization of this system that emphasizes a distinction between the representation of invariant and changeable aspects of faces. The representation of invariant aspects of faces underlies the recognition of individuals, whereas the representation of changeable aspects of faces, such as eye gaze, expression, and lip movement, underlies the perception of information that facilitates social communication. The model is also hierarchical insofar as it is divided into a core system and an extended system. The core system is comprised of occipitotemporal regions in extrastriate visual cortex that mediate the visual analysis of faces. In the core system, the representation of invariant aspects is mediated more by the face-responsive region in the fusiform gyrus, whereas the representation of changeable aspects is mediated more by the face-responsive region in the superior temporal sulcus. The extended system is comprised of regions from neural systems for other cognitive functions that can be recruited to act in concert with the regions in the core system to extract meaning from faces.
Database Cracking

Database indices provide a non-discriminative navigational infrastructure to localize tuples of interest. Their maintenance cost is taken during database updates. In this work we study the complementary approach, addressing index maintenance as part of query processing using continuous physical reorganization, i.e., cracking the database into manageable pieces. Each query is interpreted not only as a request for a particular result set, but also as an advice to crack the physical database store into smaller pieces. Each piece is described by a query, all of which are assembled in a cracker index to speedup future search. The cracker index replaces the non-discriminative indices (e.g., B-trees and hash tables) with a discriminative index. Only database portions of past interest are easily localized. The remainder is unexplored territory and remains non-indexed until a query becomes interested. The cracker index is fully self-organized and adapts to changing query workloads. With cracking, the way data is physically stored self-organizes according to query workload. Even with a huge data set, only tuples of interest are touched, leading to significant gains in query performance. In case the focus shifts to a different part of the data, the cracker index will automatically adjust to that. We report on our design and implementation of cracking in the context of a full fledged relational system. It led to a limited enhancement to its relational algebra kernel, such that cracking could be piggy-backed without incurring too much processing overhead. Furthermore, we illustrate the ripple effect of dynamic reorganization on the query plans derived by the SQL optimizer. The experiences and results obtained are indicative of a significant reduction in system complexity with clear performance benefits. ∗Stratos Idreos is the contact author (Stratos.Idreos@cwi.nl) and a Ph.D student at CWI
DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset

In the past decade, research in person re-identification (re-id) has exploded due to its broad use in security and surveillance applications. Issues such as inter-camera viewpoint, illumination and pose variations make it an extremely difficult problem. Consequently, many algorithms have been proposed to tackle these issues. To validate the efficacy of re-id algorithms, numerous benchmarking datasets have been constructed. While early datasets contained relatively few identities and images, several large-scale datasets have recently been proposed, motivated by data-driven machine learning. In this paper, we introduce a new large-scale real-world re-id dataset, DukeMTMC4ReID, using 8 disjoint surveillance camera views covering parts of the Duke University campus. The dataset was created from the recently proposed fully annotated multi-target multi-camera tracking dataset DukeMTMC[36]. A benchmark summarizing extensive experiments with many combinations of existing re-id algorithms on this dataset is also provided for an up-to-date performance analysis.
Psychedelics as medicines for substance abuse rehabilitation: evaluating treatments with LSD, Peyote, Ibogaine and Ayahuasca.

Substances known as psychedelics, hallucinogens and entheogens have been employed in ethnomedical traditions for thousands of years, but after promising uses in the 1950's and 1960's they were largely prohibited in medical treatment and human research starting in the 1970's as part of the fallout from the war on drugs. Nonetheless, there are a number of studies which suggest that these substances have potential applications in the treatment of addictions. While these substances are generally classified as Schedule I, alleging no established medical uses and a high drug abuse potential, there is nonetheless evidence indicating they might be safe and effective tools for short term interventions in addictions treatment. Evidence suggests that the psychedelics have a much greater safety profile than the major addictive drugs, having extremely low levels of mortality, and producing little if any physical dependence. This paper reviews studies evaluating the use of LSD, peyote, ibogaine and ayahuasca in the treatment of dependencies and the possible mechanisms underlying the indications of effectiveness. Evidence suggests that these substances help assist recovery from drug dependency through a variety of therapeutic mechanisms, including a notable "after-glow" effect that in part reflects their action on the serotonin neurotransmitter system. Serotonin has been long recognized as central to the psychedelics' well-known phenomenological, physical, emotional and cognitive dynamics. These serotonin-based dynamics are directly relevant to treatment of addiction because of depressed serotonin levels found in addict populations, as well as the role of serotonin as a neuromodulators affecting many other neurotransmitter systems.
Eye gaze patterns in conversations: there is more the conversational agents than meets the eyes

In multi-agent, multi-user environments, users as well as agents should have a means of establishing who is talking to whom. In this paper, we present an experiment aimed at evaluating whether gaze directional cues of users could be used for this purpose. Using an eye tracker, we measured subject gaze at the faces of conversational partners during four-person conversations. Results indicate that when someone is listening or speaking to individuals, there is indeed a high probability that the person looked at is the person listened (p=88%) or spoken to (p=77%). We conclude that gaze is an excellent predictor of conversational attention in multiparty conversations. As such, it may form a reliable source of input for conversational systems that need to establish whom the user is speaking or listening to. We implemented our findings in FRED, a multi-agent conversational system that uses eye input to gauge which agent the user is listening or speaking to.
Combined task and motion planning through an extensible planner-independent interface layer

The need for combined task and motion planning in robotics is well understood. Solutions to this problem have typically relied on special purpose, integrated implementations of task planning and motion planning algorithms. We propose a new approach that uses off-the-shelf task planners and motion planners and makes no assumptions about their implementation. Doing so enables our approach to directly build on, and benefit from, the vast literature and latest advances in task planning and motion planning. It uses a novel representational abstraction and requires only that failures in computing a motion plan for a high-level action be identifiable and expressible in the form of logical predicates at the task level. We evaluate the approach and illustrate its robustness through a number of experiments using a state-of-the-art robotics simulator and a PR2 robot. These experiments show the system accomplishing a diverse set of challenging tasks such as taking advantage of a tray when laying out a table for dinner and picking objects from cluttered environments where other objects need to be re-arranged before the target object can be reached.
Power losses and efficiency of class-E power amplifier at any duty ratio

This paper presents an analysis of the effect of duty ratio on power loss and efficiency of the Class-E amplifier. Conduction loss for each Class-E circuit component is derived and total amplifier losses and efficiency are expressed as functions of duty ratio. Two identical 300-W Class-E amplifiers operating at 7.29 MHz are designed, constructed, and tested in the laboratory. Dependence of total efficiency upon duty ratio when using real components is derived and verified experimentally. Derived loss and efficiency equations demonstrate rapid drop in efficiency for low duty ratio (below approximately 30%). Experimental results very closely matched calculated power loss and efficiency.
Attitudes and cognitive organization.

In trying out this hypothesis we shall understand by attitude the positive or negative relationship of a person p to another person o or to an impersonal entity x which may be a situation, an event, an idea, or a thing, etc. Examples are: to like, to love, to esteem, to value, and their opposites. A positive relation of this kind will be written L, a negative one ~L. Thus, pLo means p likes, loves, or values o, or, expressed differently, o is positive for p.
Security Assessment of Code Obfuscation Based on Dynamic Monitoring in Android Things

Android-based Internet-of-Things devices with excellent compatibility and openness are constantly emerging. A typical example is Android Things that Google supports. Compatibility based on the same platform can provide more convenient personalization services centering on mobile devices, while this uniformity-based computing environment can expose many security vulnerabilities. For example, new mobile malware running on Android can instantly transition to all connected devices. In particular, the Android platform has a structural weakness that makes it easy to repackage applications. This can lead to malicious behavior. To protect mobile apps that are vulnerable to malicious activity, various code obfuscation techniques are applied to key logic. The most effective one of this kind involves safely concealing application programming interfaces (API). It is very important to ensure that obfuscation is applied to the appropriate API with an adequate degree of resistance to reverse engineering. Because there is no objective evaluation method, it depends on the developer judgment. Therefore, in this paper, we propose a scheme that can quantitatively evaluate the level of hiding of APIs, which represent the function of the Android application based on machine learning theory. To perform the quantitative evaluation, the API information is obtained by static analysis of a DEX file, and the API-called code executed in Dalvik in the Android platform is dynamically extracted. Moreover, the sensitive APIs are classified using the extracted API and Naive Bayes classification. The proposed scheme yields a high score according to the level of hiding of the classified API. We tested the proposed scheme on representative applications of the Google Play Store. We believe it can be used as a model for obfuscation assessment schemes, because it can evaluate the level of obfuscation in general without relying on specific obfuscation tools.
QRS Template Matching for Recognition of Ventricular Ectopic Beats

We propose a quasi real-time method for discrimination of ventricular ectopic beats from both supraventricular and paced beats in the electrocardiogram (ECG). The heartbeat waveforms were evaluated within a fixed-length window around the fiducial points (100 ms before, 450 ms after). Our algorithm was designed to operate with minimal expert intervention and we define that the operator is required only to initially select up to three ‘normal’ heartbeats (the most frequently seen supraventricular or paced complexes). These were named original QRS templates and their copies were substituted continuously throughout the ECG analysis to capture slight variations in the heartbeat waveforms of the patient’s sustained rhythm. The method is based on matching of the evaluated heartbeat with the QRS templates by a complex set of ECG descriptors, including maximal cross-correlation, area difference and frequency spectrum difference. Temporal features were added by analyzing the R-R intervals. The classification criteria were trained by statistical assessment of the ECG descriptors calculated for all heartbeats in MIT-BIH Supraventricular Arrhythmia Database. The performance of the classifiers was tested on the independent MIT-BIH Arrhythmia Database. The achieved unbiased accuracy is represented by sensitivity of 98.4% and specificity of 98.86%, both being competitive to other published studies. The provided computationally efficient techniques enable the fast post-recording analysis of lengthy Holter-monitor ECG recordings, as well as they can serve as a quasi real-time detection method embedded into surface ECG monitors.