nils-herrmann/clean_scirepeval huggingface dataset:
Geometric modeling and singularity of 6 DOF Fanuc 200IC robot

In this paper, we propose a solution to the problems and the features encountered in the geometric modeling of the 6 DOF manipulator arm, the Fanuc. Among these, the singularity of the Jacobian matrix obtained by the kinematic model and which has a great influence on the boundaries and accessibility of the workspace of manipulator robot and it reduce the number of solutions found. We can decompose it into several sub-matrices of smaller dimensions, for ultimately a non-linear equation with two unknowns. We validate our work by conducting a simulation software platform that allows us to verify the results of manipulation in a virtual reality environment based on VRML and Matlab software, integration with the CAD model.
Top-k Multiclass SVM

Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimize for top-k performance. Our generalization of the well-known multiclass SVM is based on a tight convex upper bound of the top-k error. We propose a fast optimization scheme based on an efficient projection onto the top-k simplex, which is of its own interest. Experiments on five datasets show consistent improvements in top-k accuracy compared to various baselines.
A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs

Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, — a “sentiment lexicon” or “affective word lists”. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) developed before the advent of microblogging and sentiment analysis. I wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs. I used manually labeled postings from Twitter scored for sentiment. Using a simple word matching I show that the new word list may perform better than ANEW, though not as good as the more elaborate approach found in SentiStrength.
Antenna-in-Package Design Based on Wafer-Level Packaging With Through Silicon Via Technology

In this paper, a CPW-fed antenna-in-package (AiP) operating at millimeter wave (mmWave) based on a wafer-level packaging technology with through silicon via (TSV) interconnections is proposed, designed, and measured. The designed antenna consists of two-stacked high-resistivity silicon (HRSi) substrates. One is the bottom HRSi substrate with thickness of 750 μm, which carries the slot radiator and the CPW feeding. The other one is the top HRSi substrate with thickness of 200 μm carrying a patch, which is placed on the radiating element for antenna gain and efficiency improvement. The vertical interconnects in this structure are designed using the TSVs built on a HRSi wafer, which are designed to carry the radio frequency (RF) signals up to mmWave. RF path transitions are carefully designed to minimize the return loss within 10 dB in the frequency band of concern. The designed AiP is fabricated and measured, and the measured results basically match the simulation results. It is demonstrated that a wider bandwidth and less-sensitive input impedance versus the fabrication process accuracy are obtained with the designed structure in this paper. The measured results show the radiation in the broadside of the structure with gain around 2.4 dBi from 76 to 93 GHz.
Recent advances in 94 GHz FMCW imaging radar development

High resolution imaging radars have come a long way since the early 90's, starting with an FAA Synthetic Vision System program at 35/94 GHz. These systems were heavy and bulky, carried a price tag of about $500K, and were only suitable for larger aircrafts at very small quantity production. Size, weight, and power constraints make 94 GHz still a preferred choice for many situational awareness applications ranging from landing in poor visibility due to fog or brown-out, to cable warning & obstacle avoidance and sense and avoid for unmanned aerial systems. Using COTS components and highly integrated MMIC modules, a complete radar breadboard has been demonstrated in 9 months in one line replacement unit with a total weight of 20 lbs. The new generation of this 94 GHz FMCW imaging sensor will be on the order of 15 lbs or less including the entire radar signal processor. The size and weight achievements of this sensor open up the potential market for rotorcrafts and general aviation.
RDF data exploration and visualization

We present Paged Graph Visualization (PGV), a new semi-autonomous tool for RDF data exploration and visualization. PGV consists of two main components: a) the "PGV explorer" and b) the "RDF pager" module utilizing BRAHMS, our high per-formance main-memory RDF storage system. Unlike existing graph visualization techniques which attempt to display the entire graph and then filter out irrelevant data, PGV begins with a small graph and provides the tools to incrementally explore and visualize relevant data of very large RDF ontologies. We implemented several techniques to visualize and explore hot spots in the graph, i.e. nodes with large numbers of immediate neighbors. In response to the user-controlled, semantics-driven direction of the exploration, the PGV explorer obtains the necessary sub-graphs from the RDF pager and enables their incremental visualization leaving the previously laid out sub-graphs intact. We outline the problem of visualizing large RDF data sets, discuss our interface and its implementation, and through a controlled experiment we show the benefits of PGV.
Quantum computers

Over the past several decades, quantum information science has emerged to seek answers to the question: can we gain some advantage by storing, transmitting and processing information encoded in systems that exhibit unique quantum properties? Today it is understood that the answer is yes, and many research groups around the world are working towards the highly ambitious technological goal of building a quantum computer, which would dramatically improve computational power for particular tasks. A number of physical systems, spanning much of modern physics, are being developed for quantum computation. However, it remains unclear which technology, if any, will ultimately prove successful. Here we describe the latest developments for each of the leading approaches and explain the major challenges for the future.
Packet Classification on Multiple Fields

Routers classify packets to determine which flow they belong to, and to decide what service they should receive. Classification may, in general, be based on an arbitrary number of fields in the packet header. Performing classification quickly on an arbitrary number of fields is known to be difficult, and has poor worst-case performance. In this paper, we consider a number of classifiers taken from real networks. We find that the classifiers contain considerable structure and redundancy that can be exploited by the classification algorithm. In particular, we find that a simple multi-stage classification algorithm, called RFC (recursive flow classification), can classify 30 million packets per second in pipelined hardware, or one million packets per second in software.
Towards Detecting Clusters of Players using Visual and Gameplay Behavioral Cues

and associating them with player profile characteristics, demographics and specific interests and needs is of vital importance for creating content, fine tuned and optimized in such a way that user engagement and interest are maximized. This paper attempts to address the issue of visual features and player performance, as input parameters. Following an unsupervised scheme, in this work, we utilize data from Super Mario game recordings and explore the possibility of retrieving classes of player types along with existing correlations with certain global characteristics.
A translation system for enabling data mining applications on GPUs

Modern GPUs offer much computing power at a very modest cost. Even though CUDA and other related recent developments are accelerating the use of GPUs for general purpose applications, several challenges still remain in programming the GPUs. Thus, it is clearly desirable to be able to program GPUs using a higher-level interface.
 In this paper, we offer a solution that targets a specific class of applications, which are the data mining and scientific data analysis applications. Our work is driven by the observation that a common processing structure, that of generalized reductions, fits a large number of popular data mining algorithms. In our solution, the programmers simply need to specify the sequential reduction loop(s) with some additional information about the parameters. We use program analysis and code generation to map the applications to a GPU. Several additional optimizations are also performed by the system.
 We have evaluated our system using three popular data mining applications, k-means clustering, EM clustering, and Principal Component Analysis (PCA). The main observations from our experiments are as follows. The speedup that each of these applications achieve over a sequential CPU version ranges between 20 and 50. The automatically generated version did not have any noticeable overheads compared to hand written codes. Finally, the optimizations performed in the system resulted in significant performance improvements.
Modeling affordances using Bayesian networks

Affordances represent the behavior of objects in terms of the robot's motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic systems, since it is at the core of many higher level skills such as imitation. In this paper, we propose a general affordance model based on Bayesian networks linking actions, object features and action effects. The network is learnt by the robot through interaction with the surrounding objects. The resulting probabilistic model is able to deal with uncertainty, redundancy and irrelevant information. We evaluate the approach using a real humanoid robot that interacts with objects.
A Survey on Software Clone Detection Research

Code duplication or copying a code fragment and then reuse by pasting with or without any modifications is a well known code smell in software maintenance. Several studies show that about 5% to 20% of a software systems can contain duplicated code, which is basically the results of copying existing code fragments and using then by pasting with or without minor modifications. One of the major shortcomings of such duplicated fragments is that if a bug is detected in a code fragment, all the other fragments similar to it should be investigated to check the possible existence of the same bug in the similar fragments. Refactoring of the duplicated code is another prime issue in software maintenance although several studies claim that refactoring of certain clones are not desirable and there is a risk of removing them. However, it is also widely agreed that clones should at least be detected. In this paper, we survey the state of the art in clone detection research. First, we describe the clone terms commonly used in the literature along with their corresponding mappings to the commonly used clone types. Second, we provide a review of the existing clone taxonomies, detection approaches and experimental evaluations of clone detection tools. Applications of clone detection research to other domains of software engineering and in the same time how other domain can assist clone detection research have also been pointed out. Finally, this paper concludes by pointing out several open problems related to clone detection research. ∗This document represents our initial findings and a further study is being carried on. Reader’s feedback is welcome at croy@cs.queensu.ca.
Understanding the Mirai Botnet

The Mirai botnet, composed primarily of embedded and IoT devices, took the Internet by storm in late 2016 when it overwhelmed several high-profile targets with massive distributed denial-of-service (DDoS) attacks. In this paper, we provide a seven-month retrospective analysis of Mirai’s growth to a peak of 600k infections and a history of its DDoS victims. By combining a variety of measurement perspectives, we analyze how the botnet emerged, what classes of devices were affected, and how Mirai variants evolved and competed for vulnerable hosts. Our measurements serve as a lens into the fragile ecosystem of IoT devices. We argue that Mirai may represent a sea change in the evolutionary development of botnets—the simplicity through which devices were infected and its precipitous growth, demonstrate that novice malicious techniques can compromise enough low-end devices to threaten even some of the best-defended targets. To address this risk, we recommend technical and nontechnical interventions, as well as propose future research directions.
Automatically Detecting and Rating Product Aspects from Textual Customer Reviews

This paper proposes a new approach to aspect-based sentiment analysis. The goal of our algorithm is to obtain a summary of the most positive and the most negative aspects of a specific product, given a collection of free-text customer reviews. Our approach starts by matching handcrafted dependency paths in individual sentences to find opinions expressed towards candidate aspects. Then, it clusters together different mentions of the same aspect by using a WordNet-based similarity measure. Finally, it computes a sentiment score for each aspect, which represents the overall emerging opinion of a group of customers towards a specific aspect of the product. Our approach does not require any seed word or domain-specific knowledge, as it only employs an offthe-shelf sentiment lexicon. We discuss encouraging preliminary results in detecting and rating aspects from on-line reviews of movies and MP3 players.
A survey of new trends in symbolic execution for software testing and analysis

Symbolic execution is a well-known program analysis technique which represents program inputs with symbolic values instead of concrete, initialized, data and executes the program by manipulating program expressions involving the symbolic values. Symbolic execution has been proposed over three decades ago but recently it has found renewed interest in the research community, due in part to the progress in decision procedures, availability of powerful computers and new algorithmic developments. We provide here a survey of some of the new research trends in symbolic execution, with particular emphasis on applications to test generation and program analysis. We first describe an approach that handles complex programming constructs such as input recursive data structures, arrays, as well as multithreading. Furthermore, we describe recent hybrid techniques that combine concrete and symbolic execution to overcome some of the inherent limitations of symbolic execution, such as handling native code or availability of decision procedures for the application domain. We follow with a discussion of techniques that can be used to limit the (possibly infinite) number of symbolic configurations that need to be analyzed for the symbolic execution of looping programs. Finally, we give a short survey of interesting new applications, such as predictive testing, invariant inference, program repair, analysis of parallel numerical programs and differential symbolic execution.
Toward automatic reconstruction of interiors from laser data

So far, realistic models of interiors have always been designed manually with the help of dedicated software packages. However, the demand for indoor models for different purposes has recently increased, thus a higher degree of automation could better satisfy different applications and speed up the processes. We present a technique for the fully automated modelling of indoor environments from a three dimensional point cloud. The results we achieve are very promising and the method suggested may provide completion to the actual standard for 3D city modelling. Our approach is based on a plane sweep algorithm for the segmentation of a point cloud in order to recognize the planar structures of a room. At first the 3D points that belong to the horizontal structures are tagged by sweeping a virtual plane along the vertical direction and thresholding the distances of each point to the plane. All the points that are not chosen as either floor or ceiling are labelled as potential wall points and are being considered in the following segmentation step to detect the vertical faces. Finally, the floor plan of the room is estimated by intersecting the directions of the walls and finding the vertices that constitute the ground shape. The result generated is a 3D model in CAD format, which perfectly fits the original point cloud.
Differentiating ability in users of the ReWalkTM powered exoskeleton: An analysis of walking kinematics

The ReWalkTM powered exoskeleton assists thoracic level motor complete spinal cord injury patients who are paralyzed to walk again with an independent, functional, upright, reciprocating gait. We completed an evaluation of twelve such individuals with promising results. All subjects met basic criteria to be able to use the ReWalkTM - including items such as sufficient bone mineral density, leg passive range of motion, strength, body size and weight limits. All subjects received approximately the same number of training sessions. However there was a wide distribution in walking ability. Walking velocities ranged from under 0.1m/s to approximately 0.5m/s. This variability was not completely explained by injury level The remaining sources of that variability are not clear at present. This paper reports our preliminary analysis into how the walking kinematics differed across the subjects - as a first step to understand the possible contribution to the velocity range and determine if the subjects who did not walk as well could be taught to improve by mimicking the better walkers.
Timing-sync protocol for sensor networks

Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. The applications envisioned for such networks require collaborative execution of a distributed task amongst a large set of sensor nodes. This is realized by exchanging messages that are timestamped using the local clocks on the nodes. Therefore, time synchronization becomes an indispensable piece of infrastructure in such systems. For years, protocols such as NTP have kept the clocks of networked systems in perfect synchrony. However, this new class of networks has a large density of nodes and very limited energy resource at every node; this leads to scalability requirements while limiting the resources that can be used to achieve them. A new approach to time synchronization is needed for sensor networks. In this paper, we present Timing-sync Protocol for Sensor Networks (TPSN) that aims at providing network-wide time synchronization in a sensor network. The algorithm works in two steps. In the first step, a hierarchical structure is established in the network and then a pair wise synchronization is performed along the edges of this structure to establish a global timescale throughout the network. Eventually all nodes in the network synchronize their clocks to a reference node. We implement our algorithm on Berkeley motes and show that it can synchronize a pair of neighboring motes to an average accuracy of less than 20μs. We argue that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes. We also show the performance of TPSN over small multihop networks of motes and use simulations to verify its accuracy over large-scale networks. We show that the synchronization accuracy does not degrade significantly with the increase in number of nodes being deployed, making TPSN completely scalable.
Variable threshold voltage CMOS (VTCMOS) in series connected circuits

Characteristics of variable threshold voltage CMOS (VTCMOS) in the series connected circuits are investigated by means of device simulation. It is newly found that the performance degradation due to the body effect in series connected circuit is suppressed by utilizing VTCMOS. Lowering the threshold voltage (Vth) enhances the drive current and alleviates the degradation due to the series connected configuration. Therefore, larger body effect factor (γ) results in lower Vth and higher oncurrent even in the series connected circuits. These characteristics are attributed to the velocity saturation phenomenon which reduces the drain saturation voltage (Vdsat).
Anti-Forensics : Techniques , Detection and Countermeasures

Computer Forensic Tools (CFTs) allow investigators to recover deleted files, reconstruct an intruder’s activities, and gain intelligence about a computer’s user. Anti-Forensics (AF) tools and techniques frustrate CFTs by erasing or altering information; creating “chaff” that wastes time and hides information; implicating innocent parties by planting fake evidence; exploiting implementation bugs in known tools; and by leaving “tracer” data that causes CFTs to inadvertently reveal their use to the attacker. Traditional AF tools like disk sanitizers were created to protect the privacy of the user. Anti-debugging techniques were designed to protect the intellectual property of compiled code. Rootkits allow attackers to hide their tools from other programs running on the same computer. But in recent years there has been an emergence of AF that directly target CFTs. This paper categorizes traditional AF techniques such as encrypted file systems and disk sanitization utilities, and presents a survey of recent AF tools including Timestomp and Transmogrify. It discusses approaches for attacking forensic tools by exploiting bugs in those tools, as demonstrated by the “42.zip” compression bomb. Finally, it evaluates the effectiveness of these tools for defeating CFTs, presents strategies for their detection, and discusses countermeasures.
Individual differences in adult decision-making competence.

The authors evaluated the reliability and validity of a set of 7 behavioral decision-making tasks, measuring different aspects of the decision-making process. The tasks were administered to individuals from diverse populations. Participants showed relatively consistent performance within and across the 7 tasks, which were then aggregated into an Adult Decision-Making Competence (A-DMC) index that showed good reliability. The validity of the 7 tasks and of overall A-DMC emerges in significant relationships with measures of socioeconomic status, cognitive ability, and decision-making styles. Participants who performed better on the A-DMC were less likely to report negative life events indicative of poor decision making, as measured by the Decision Outcomes Inventory. Significant predictive validity remains when controlling for demographic measures, measures of cognitive ability, and constructive decision-making styles. Thus, A-DMC appears to be a distinct construct relevant to adults' real-world decisions.