nils-herrmann/clean_scirepeval huggingface dataset:
The capacity of wireless networks

When identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput ( ) obtainable by each node for a randomly chosen destination is log bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission’s range is optimally chosen, the bit–distance product that can be transported by the network per second is ( ) bit-meters per second. Thus even under optimal circumstances, the throughput is only bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.
BioBench: A Benchmark Suite of Bioinformatics Applications

Recent advances in bioinformatics and the significant increase in computational power available to researchers have made it possible to make better use of the vast amounts of genetic data that has been collected over the last two decades. As the uses of genetic data expand to include drug discovery and development of gene-based therapies, bioinformatics is destined to take its place in the forefront of scientific computing application domains. Despite the clear importance of this field, common bioinformatics applications and their implication on microarchitectural design have received scant attention from the computer architecture community so far. The availability of a common set of bioinformatics benchmarks could be the first step to motivate further research in this crucial area. To this end, this paper presents BioBench, a benchmark suite that represents a diverse set of bioinformatics applications. The first version of BioBench includes applications from different application domains, with a particular emphasis on mature genomics applications. The applications in the benchmark are described briefly, and basic execution characteristics obtained on a real processor are presented. Compared to SPEC INT and SPEC FP benchmarks, applications in BioBench display a higher percentage of load/store instructions, almost negligible floating-point operation content, and higher IPC than either SPEC INT or SPEC FP applications. Our evaluation suggests that bioinformatics applications have distinctly different characteristics from the applications in both of the mentioned SPEC suites; and our findings indicate that bioinformatics workloads can benefit from architectural improvements to memory bandwidth and techniques that exploit their high levels of ILP. The entire BioBench suite and accompanying reference data will be made freely available to researchers
An architecture for a generic dialogue shell

1 Introduction Different researchers use the term " dialogue system " in different ways with little in common beyond the belief that a dialogue system necessarily interacts with a human. For some, a dialogue system involves some mechanism to constrain the interaction, such as a script that specifies system prompts and user responses. The most common examples of this type of system in use are the telephone interfaces based on keyed or spoken menu selection (e.g., " if you want your account balance, press or say 1, " and so on). The goal here is to restrict the user's options in the interaction to simplify the language processing. For others, dialogue work aims to produce machines that can mimic human conversation. Work in this area aims to provide intuitive access to a wide range of applications, either over the telephone using voice only, or with multi-modal interaction at a computer workstation. The goal of this approach is to expand the user's options in the interaction. We have the latter goal. We believe that such systems could be both feasible and cost-effective within the next decade. While speech-driven " menu-style " dialogue systems are growing in use today, they have their limitations. Unless the user needs to perform the most common and expected tasks, and can adapt to the preset method of performing those tasks, such interfaces can be a source of frustration. In addition, it appears that more complex tasks will be impossible to perform using such limited dialogue interaction. To support effective dialogue, the input language must be sufficiently general to express a wide range of different goals and courses of action. Effective interaction also requires an ability to support " mixed-initiative " interaction. The user must be able to " lead " the conversation in ways that best accomplish their goals, and the system should be able to take the initiative to speed up the solution when opportunities arise. In all but the simplest tasks, the system must be able to perform intention recognition (to determine what part of the task is currently being performed) and it be able to track the flow of topic as it changes throughout the dialogue. The system must also be able to plan responses that can only be realized incrementally over several turns in the dialogue. Finally, the system must be concerned with grounding: it must efficiently signal that the user was …
A Warm Welcome Matters!: The Link Between Social Feedback and Weight Loss in /r/loseit

Social feedback has long been recognized as an important element of successful health-related behavior change. However, most of the existing studies look at the effect that offline social feedback has. This paper fills gaps in the literature by proposing a framework to study the causal effect that receiving social support in the form of comments in an online weight loss community has on (i) the probability of the user to return to the forum, and, more importantly, on (ii) the weight loss reported by the user. Using a matching approach for causal inference we observe a difference of 9 lbs lost between users who do or do not receive comments. Surprisingly, this effect is mediated by neither an increase in lifetime in the community nor by an increased activity level of the user. Our results show the importance that a “warm welcome” has when using online support forums to achieve health outcomes.
Deep SimNets

We present a deep layered architecture that generalizes convolutional neural networks (ConvNets). The architecture, called SimNets, is driven by two operators: (i) a similarity function that generalizes inner-product, and (ii) a log-mean-exp function called MEX that generalizes maximum and average. The two operators applied in succession give rise to a standard neuron but in "feature space". The feature spaces realized by SimNets depend on the choice of the similarity operator. The simplest setting, which corresponds to a convolution, realizes the feature space of the Exponential kernel, while other settings realize feature spaces of more powerful kernels (Generalized Gaussian, which includes as special cases RBF and Laplacian), or even dynamically learned feature spaces (Generalized Multiple Kernel Learning). As a result, the SimNet contains a higher abstraction level compared to a traditional ConvNet. We argue that enhanced expressiveness is important when the networks are small due to run-time constraints (such as those imposed by mobile applications). Empirical evaluation validates the superior expressiveness of SimNets, showing a significant gain in accuracy over ConvNets when computational resources at run-time are limited. We also show that in large-scale settings, where computational complexity is less of a concern, the additional capacity of SimNets can be controlled with proper regularization, yielding accuracies comparable to state of the art ConvNets.
SCP: A Computationally-Scalable Byzantine Consensus Protocol For Blockchains

In this paper, we design a new blockchain Byzantine consensus protocol SCP where the throughput scales nearly linearly with the computation: the more computing power available, the more blocks selected per unit time. SCP is also efficient that the number of messages it requires is nearly linear in the network size. The computational scalability property offers the flexibility to tune bandwidth consumption by adjusting computational parameters (e.g., proof-of-work difficulty). The key ideas lie in securely establishing identities for network participants, randomly placing them in several committees and running a classical consensus protocol within each committee to propose blocks in parallel. We further design a mechanism to allow reaching consensus on blocks without broadcasting actual block data, while still enabling efficient block verification. We prove that our protocol is secure, efficient and applicable to several case studies. We conduct scalability experiments on Amazon EC2 with upto 80 cores, and confirm that SCP matches its theoretical scaling properties.
Dynamic path planning and replanning for mobile robots using RRT

It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT∗ is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method.
Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition

Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the advantages of using higherorder correlations, specifically, we introduce Higher-order Kernel (HOK) descriptors generated from the late fusion of CNN classifier scores from all the frames in a sequence. To generate these descriptors, we use the idea of kernel linearization. Specifically, a similarity kernel matrix, which captures the temporal evolution of deep classifier scores, is first linearized into kernel feature maps. The HOK descriptors are then generated from the higher-order cooccurrences of these feature maps, and are then used as input to a video-level classifier. We provide experiments on two fine-grained action recognition datasets, and show that our scheme leads to state-of-the-art results.
A Training Algorithm for Optimal Margin Classifiers

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
Multi Factor Authentication Using Mobile Phones

This paper describes a method of implementing two factor authentication using mobile phones. The proposed method guarantees that authenticating to services, such as online banking or ATM machines, is done in a very secure manner. The proposed system involves using a mobile phone as a software token for One Time Password generation. The generated One Time Password is valid for only a short user-defined period of time and is generated by factors that are unique to both, the user and the mobile device itself. Additionally, an SMSbased mechanism is implemented as both a backup mechanism for retrieving the password and as a possible mean of synchronization. The proposed method has been implemented and tested. Initial results show the success of the proposed method.
Crafting Adversarial Examples For Speech Paralinguistics Applications

Computational paralinguistic analysis is increasingly being used in a wide range of applications, including securitysensitive applications such as speaker verification, deceptive speech detection, and medical diagnostics. While state-ofthe-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. In this work, we propose a novel end-to-end scheme to generate adversarial examples by perturbing directly the raw waveform of an audio recording rather than specific acoustic features. Our experiments show that the proposed adversarial perturbation can lead to a significant performance drop of state-of-the-art deep neural networks, while only minimally impairing the audio quality.
Game Theory in Signal Processing and Communications

1Department of Mobile Communications, School of Electrical Engineering and Computer Sciences, Technical University of Berlin, Berlin, Germany 2Wireless Networking, Signal Processing and Security Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 3Division of Communication Systems, Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden 4Communications Laboratory, Faculty of Electrical Engineering and Information Technology, Dresden University of Technology, 01062 Dresden, Germany
Parallel geometric algorithms for multi-core computers

Computers with multiple processor cores using shared memory are now ubiquitous. In this paper, we present several parallel geometric algorithms that specifically target this environment, with the goal of exploiting the additional computing power. The d-dimensional algorithms we describe are (a) spatial sorting of points, as is typically used for preprocessing before using incremental algorithms, (b) kd-tree construction, (c) axis-aligned box intersection computation, and finally (d) bulk insertion of points in Delaunay triangulations for mesh generation algorithms or simply computing Delaunay triangulations. We show experimental results for these algorithms in 3D, using our implementations based on the Computational Geometry Algorithms Library (CGAL, http://www.cgal.org/). This work is a step towards what we hope will become a parallel mode for CGAL, where algorithms automatically use the available parallel resources without requiring significant user intervention.
The Generative Lexicon

In this paper, I will discuss four major topics relating to current research in lexical semantics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best approaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is performed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole.
Flexible Deterministic Packet Marking: An IP Traceback System to Find the Real Source of Attacks

IP traceback is the enabling technology to control Internet crime. In this paper we present a novel and practical IP traceback system called Flexible Deterministic Packet Marking (FDPM) which provides a defense system with the ability to find out the real sources of attacking packets that traverse through the network. While a number of other traceback schemes exist, FDPM provides innovative features to trace the source of IP packets and can obtain better tracing capability than others. In particular, FDPM adopts a flexible mark length strategy to make it compatible to different network environments; it also adaptively changes its marking rate according to the load of the participating router by a flexible flow-based marking scheme. Evaluations on both simulation and real system implementation demonstrate that FDPM requires a moderately small number of packets to complete the traceback process; add little additional load to routers and can trace a large number of sources in one traceback process with low false positive rates. The built-in overload prevention mechanism makes this system capable of achieving a satisfactory traceback result even when the router is heavily loaded. It has been used to not only trace DDoS attacking packets but also enhance filtering attacking traffic.
Activity sensing in the wild: a field trial of ubifit garden

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.
Analyzing the Effectiveness and Applicability of Co-training

Recently there has been signi cant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting  applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminative in nature and robust to the assumptions of their embedded classi ers.
Joint SFM and detection cues for monocular 3D localization in road scenes

We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.
How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications

In this paper an analysis of the presence and possibilities of altmetrics for bibliometric and performance analysis is carried out. Using the web based tool Impact Story, we collected metrics for 20,000 random publications from the Web of Science. We studied both the presence and distribution of altmetrics in the set of publications, across fields, document types and over publication years, as well as the extent to which altmetrics correlate with citation indicators. The main result of the study is that the altmetrics source that provides the most metrics is Mendeley, with metrics on readerships for 62.6 % of all the publications studied, other sources only provide marginal information. In terms of relation with citations, a moderate spearman correlation (r = 0.49) has been found between Mendeley readership counts and citation indicators. Other possibilities and limitations of these indicators are discussed and future research lines are outlined.
A 1.6Gbps Digital Clock and Data Recovery Circuit

A digital clock and data recovery circuit employs simple 3-level digital-to-analog converters to interface the digital loop filter to the voltage controlled oscillator and achieves low jitter performance. Test chip fabricated in a 0.13mum CMOS process achieves BER < 10-12 , plusmn1500ppm lock-in range, plusmn2500ppm tracking range, recovered clock jitter of 8.9ps rms and consumes 12mW power from a single-pin 1.2V supply, while operating at 1.6Gbps
What ( and Why ) Is Positive Psychology ?

Positive psychology is the study of the conditions and processes that contribute to the flourishing or optimal functioning of people, groups, and institutions. In this brief introduction, the authors give examples of current work in positive psychology and try to explain why the positive psychology movement has grown so quickly in just 5 years. They suggest that it filled a need: It guided researchers to understudied phenomena. The authors close by addressing some criticisms and shortcomings of positive psychology, such as the relative lack of progress in studying positive institutions.
Marmara Turkish Coreference Corpus and Coreference Resolution Baseline

We describe the Marmara Turkish Coreference Corpus, which is an annotation of the whole METU-Sabanci Turkish Treebank with mentions and coreference chains. Collecting nine or more independent annotations for each document allowed for fully automatic adjudication. We provide a baseline system for Turkish mention detection and coreference resolution and evaluate it on the corpus.