nils-herrmann/clean_scirepeval huggingface dataset:
Strong Authentication for RFID Systems Using the AES Algorithm

Radio frequency identification (RFID) is an emerging technology which brings enormous productivity benefits in applications where objects have to be identified automatically. This paper presents issues concerning security and privacy of RFID systems which are heavily discussed in public. In contrast to the RFID community, which claims that cryptographic components are too costly for RFID tags, we describe a solution using strong symmetric authentication which is suitable for today’s requirements regarding low power consumption and low die-size. We introduce an authentication protocol which serves as a proof of concept for authenticating an RFID tag to a reader device using the Advanced Encryption Standard (AES) as cryptographic primitive. The main part of this work is a novel approach of an AES hardware implementation which encrypts a 128-bit block of data within 1000 clock cycles and has a power consumption below 9 μA on a 0.35 μm CMOS process.
A Comparison of Features for Android Malware Detection

With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware. We are able to differentiate between benign and malicious apps by applying a machine learning algorithm. The model that is presented here achieved a classification accuracy of around 80% using permissions and 60% using system calls for a relatively small dataset. In the future, different machine learning algorithms will be examined to see if there is a more suitable algorithm. More features will also be taken into account and the training set will be expanded.
A Superior Current Source with Improved Bandwidth and Output Impedance for Bioimpedance Spectroscopy

Electrical Bio-impedance Measurement (EBM)has been widely used as a non-invasive technique for characterizing physiological and pathological status of biological tissues. Voltage Controlled Current Source (VCCS) an important part of the measurement system and its performance directly affects the precision of the measurement. Most widely used current sources in EBM are based on the Howland circuit. In this work we analyze and simulate the driving capability and operating bandwidth of the improved Howland current source. The objective is to design an Operational Transconductance Amplifier (OTA) based VCCS and to compare its performance with other widely used current sources for EBM. The results show that the proposed current source is stable,has wider output current frequency response and output impedance much larger than other current sources for wide frequency range (10kHz to 10MHz).
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net

Parsing articulated objects, e.g . humans and animals, into semantic parts (e.g . body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a “Hierarchical Auto-Zoom Net” (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of two “Auto-Zoom Nets” (AZNs), each employing fully convolutional networks that perform two tasks: (1) predict the locations and scales of object instances (the first AZN) or their parts (the second AZN); (2) estimate the part scores for predicted object instance or part regions. Our model can adaptively “zoom” (resize) predicted image regions into their proper scales to refine the parsing. We conduct extensive experiments over the PASCAL part datasets on humans, horses, and cows. For humans, our approach significantly outperforms the state-of-the-arts by 5% mIOU and is especially better at segmenting small instances and small parts. We obtain similar improvements for parsing cows and horses over alternative methods. In summary, our strategy of first zooming into objects and then zooming into parts is very effective. It also enables us to process different regions of the image at different scales adaptively so that, for example, we do not need to waste computational resources scaling the entire image.
1 ACO Algorithms for the Traveling Salesman Problemy

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect communication of a colony of agents, called (artificial) ants, based on pheromone trails (pheromones are also used by real ants for communication). The (artificial) pheromone trails are a kind of distributed numeric information which is modified by the ants to reflect their experience while solving a particular problem. Recently, the Ant Colony Optimization (ACO) metaheuristic has been proposed which provides a unifying framework for most applications of ant algorithms [15, 16] to combinatorial optimization problems. In particular, all the ant algorithms applied to the TSP fit perfectly into the ACO meta-heuristic and, therefore, we will call these algorithms also ACO algorithms. The first ACO algorithm, called Ant System (AS) [18, 14, 19], has been applied to the Traveling Salesman Problem (TSP). Starting from Ant System, several improvements of the basic algorithm have been proposed [21, 22, 17, 51, 53, 7]. Typically, these improved algorithms have been tested again on the TSP. All these improved versions of AS have in common a stronger exploita-
A Systematic Review on Mobile Health Care

This paper presents the state of the art from the available literature on mobile health care. The study was performed by means of a systematic review, a way of assessing and interpreting all available research on a particular topic in a given area, using a reliable and rigorous method. From an initial amount of 1,482 papers, we extracted and analysed data via a full reading of 40 (2.69%) of the papers that matched our selection criteria. Our analysis since 2010 show current development in 10 application areas and present ongoing trends and technical hallenges on the subject. The application areas include: patient monitoring, infrastructure, software architecture, modeling, framework, security, fications, multimedia, mobile cloud computing, and literature reviews on the topic. The most relevant challenges include the low battery life of devices, ultiplatform development, data transmission and security. Our paper consolidates ecent findings in the field and serves as a resourceful guide for future research planning and development.
Mining the network value of customers

One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected profit from sales to her). We propose to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a collaborative filtering database. Marketing that exploits the network value of customers---also known as viral marketing---can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.
All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask)

Dynamic taint analysis and forward symbolic execution are quickly becoming staple techniques in security analyses. Example applications of dynamic taint analysis and forward symbolic execution include malware analysis, input filter generation, test case generation, and vulnerability discovery. Despite the widespread usage of these two techniques, there has been little effort to formally define the algorithms and summarize the critical issues that arise when these techniques are used in typical security contexts. The contributions of this paper are two-fold. First, we precisely describe the algorithms for dynamic taint analysis and forward symbolic execution as extensions to the run-time semantics of a general language. Second, we highlight important implementation choices, common pitfalls, and considerations when using these techniques in a security context.
Finding Contradictions in Text

In this paper, I seek to understand the ways contradictions occur across texts and I describe a system for automatically detecting such constructions. Finding conflicting statements is foundational for text understanding, a problem which recently received a surge of interest in the computational linguistics community. Condoravdi et al. (2003) first recognized the importance of handling both entailment and contradiction for text understanding: “relations of entailment and contradiction are the key data of semantics, as traditionally viewed as a branch of linguistics. The ability to recognize such semantic relations is clearly not a sufficient criterion for language understanding: there is more than just being able to tell that one sentence follows from another. But we would argue that it is a minimal, necessary criterion.” (p. 38). So far however, work in robust text understanding has focused on entailment: systems aimed at providing textual inference in arbitrary domains. The task of textual inference first appeared latent within the field of question answering (Pasca & Harabagiu 2001; Moldovan et al. 2003). As schematized in Figure 1, the question What company sells most greetings cards? can be viewed as a statement containing a variable (what company) which in this case is of the organization type. If the system finds a text passage that entails the statement and contains a possible assignment for the variable (mainly a concept of the same type), the variable assignment is taken as the answer to the question. In this example, the passage Hallmark remains the largest maker of greetings cards entails the question, Hallmark is of the type organization, and will be the answer to the question. The task of textual inference then received attention within the PASCAL Recognizing Textual Entailment (RTE) Challenges (Dagan et al. 2006; Bar-Haim et al. 2006; Giampiccolo et al. 2007), and related work within the U.S. Government AQUAINT program. In the RTE challenges, systems are given pairs of sentences, called text (T) and hypothesis (H), and the goal is to identify whether the hypothesis follows from the text and general background knowledge, according to the intuitions of an intelligent human reader. That is, the standard is not whether the hypothesis is logically entailed, but whether it can reasonably be inferred: “We say that T entails H if the meaning of H can be inferred from the meaning of T, as would typically be interpreted by people. This somewhat informal definition is based on (and assumes) common human understanding of language as well as common background knowledge.” (Dagan et al. 2005, p. 1).
Medicinal and pharmaceutical uses of seaweed natural products: A review

In the last three decades the discovery of metabolites with biological activities from macroalgae has increased significantly. However, despite the intense research effort by academic and corporate institutions, very few products with real potential have been identified or developed. Based on Silverplatter MEDLINE and Aquatic Biology, Aquaculture & Fisheries Resources databases, the literature was searched for natural products from marine macroalgae in the Rhodophyta, Phaeophyta and Chlorophyta with biological and pharmacological activity. Substances that currently receive most attention from pharmaceutical companies for use in drug development, or from researchers in the field of medicine-related research include: sulphated polysaccharides as antiviral substances, halogenated furanones from Delisea pulchra as antifouling compounds, and kahalalide F from a species of Bryopsis as a possible treatment of lung cancer, tumours and AIDS. Other substances such as macroalgal lectins, fucoidans, kainoids and aplysiatoxins are routinely used in biomedical research and a multitude of other substances have known biological activities. The potential pharmaceutical, medicinal and research applications of these compounds are discussed.
DyNet: The Dynamic Neural Network Toolkit

We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives. In DyNet’s dynamic declaration strategy, computation graph construction is mostly transparent, being implicitly constructed by executing procedural code that computes the network outputs, and the user is free to use different network structures for each input. Dynamic declaration thus facilitates the implementation of more complicated network architectures, and DyNet is specifically designed to allow users to implement their models in a way that is idiomatic in their preferred programming language (C++ or Python). One challenge with dynamic declaration is that because the symbolic computation graph is defined anew for every training example, its construction must have low overhead. To achieve this, DyNet has an optimized C++ backend and lightweight graph representation. Experiments show that DyNet’s speeds are faster than or comparable with static declaration toolkits, and significantly faster than Chainer, another dynamic declaration toolkit. DyNet is released opensource under the Apache 2.0 license, and available at http://github.com/clab/dynet. Carnegie Mellon University, Pittsburgh, PA, USA Nara Institute of Science and Technology, Ikoma, Japan DeepMind, London, UK Bar Ilan University, Ramat Gan, Israel Allen Institute for Artificial Intelligence, Seattle, WA, USA University of Notre Dame, Notre Dame, IN, USA IBM T.J. Watson Research Center, Yorktown Heights, NY, USA University of Melbourne, Melbourne, Australia Johns Hopkins University, Baltimore, MD, USA Google, New York, NY, USA Google, Mountain View, CA, USA University of Washington, Seattle, USA Microsoft Research, Redmond, WA, USA University of Edinburgh, Edinburgh, UK 1 ar X iv :1 70 1. 03 98 0v 1 [ st at .M L ] 1 5 Ja n 20 17
Growing a tree in the forest: constructing folksonomies by integrating structured metadata

Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.
An Efficient Algorithm for Local Distance Metric Learning

Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, such distance metrics attempt to keep all of the data points in each class close together while ensuring that data points from different classes are separated. However, particularly when classes exhibit multimodal data distributions, these goals conflict and thus cannot be simultaneously satisfied. This paper proposes a Local Distance Metric (LDM) that aims to optimize local compactness and local separability. We present an efficient algorithm that employs eigenvector analysis and bound optimization to learn the LDM from training data in a probabilistic framework. We demonstrate that LDM achieves significant improvements in both classification and retrieval accuracy compared to global distance learning and kernel-based KNN.  Distance metric learning has played a significant role in both statistical classification and information retrieval. For instance, previous studies (Goldberger et al. 2005; Weinberger, Blitzer, & Saul 2006) have shown that appropriate distance metrics can significantly improve the classification accuracy of the K Nearest Neighbor (KNN) algorithm. In multimedia information retrieval, several papers (He et al. 2003; 2004; Muller, Pun, & Squire 2004) have shown that appropriate distance metrics, learned either from labeled or unlabeled data, usually result in substantial improvements in retrieval accuracy compared to the standard Euclidean distance. Most of the work in distance metrics learning can be organized into the following two categories: • Unsupervised distance metric learning, or manifold learning. The main idea is to learn a underlying low-dimensional manifold where geometric relationships (e.g., distance) between most of the observed data points are preserved. Popular algorithms in this category include ISOMAP (Tenenbaum, de Silva, & Langford 2000), Local Linear Embedding (Saul & Roweis 2003), and the Laplacian Eigenmap (Belkin & Niyogi 2003).  −4 −2 0 2 4 6 8 10 12 Class A Class B (a) −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 −0.25 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 Class A Class B
Percutaneous kyphoplasty and pedicle screw fixation for the management of thoraco-lumbar burst fractures

The study design includes prospective evaluation of percutaneous osteosynthesis associated with cement kyphoplasty on 18 patients. The objective of the study is to assess the efficacy of a percutaneous method of treating burst vertebral fractures in patients without neurological deficits. Even if burst fractures are frequent, no therapeutic agreement is available at the moment. We report in this study the results at 2 years with a percutaneous approach for the treatment of burst fractures. 18 patients were included in this study. All the patients had burst vertebral fractures classified type A3 on the Magerl scale, between levels T9 and L2. The patients’ mean age was 53 years (range 22–78 years) and the neurological examination was normal. A percutaneous approach was systematically used and a kyphoplasty was performed via the transpedicular pathway associated with percutaneous short-segment pedicle screw osteosynthesis. The patients’ follow-up included CT scan analysis, measurement of vertebral height recovery and local kyphosis, and clinical pain assessments. With this surgical approach, the mean vertebral height was improved by 25% and a mean improvement of 11.28° in the local kyphotic angle was obtained. 3 months after the operation, none of the patients were taking class II analgesics. The mean duration of their hospital stay was 4.5 days (range 3–7 days) and the mean follow-up period was 26 months (range 17–30 months). No significant changes in the results obtained were observed at the end of the follow-up period. Minimally invasive methods of treating burst vertebral fractures can be performed via the percutaneous pathway. This approach gives similar vertebral height recovery and kyphosis correction rates to those obtained with open surgery. It provides a short hospital stay, however, and might therefore constitute a useful alternative to open surgical methods.
Malware analysis using visualized images and entropy graphs

Today, along with the development of the Internet, the number of malicious software, or malware, distributed especially for monetary profits, is exponentially increasing, and malware authors are developing malware variants using various automated tools and methods. Automated tools and methods may reuse some modules to develop malware variants, so these reused modules can be used to classify malware or to identify malware families. Therefore, similarities may exist among malware variants can be analyzed and used for malware variant detections and the family classification. This paper proposes a new malware family classification method by converting binary files into images and entropy graphs. The experimental results show that the proposed method can effectively distinguish malware families.
New Trellis Code Design for Spatial Modulation

Spatial modulation (SM), in which multiple antennas are used to convey information besides the conventional M-ary signal constellations, is a new multiple-input multiple-output (MIMO) transmission technique, which has recently been proposed as an alternative to V-BLAST (vertical Bell Labs layered space-time). In this paper, a novel MIMO transmission scheme, called spatial modulation with trellis coding (SM-TC), is proposed. Similar to the conventional trellis coded modulation (TCM), in this scheme, a trellis encoder and an SM mapper are jointly designed to take advantage of the benefits of both. A soft decision Viterbi decoder, which is fed with the soft information supplied by the optimal SM decoder, is used at the receiver. A pairwise error probability (PEP) upper bound is derived for the SM-TC scheme in uncorrelated quasi-static Rayleigh fading channels. From the PEP upper bound, code design criteria are given and then used to obtain new 4-, 8- and 16-state SM-TC schemes using quadrature phase-shift keying (QPSK) and 8-ary phase-shift keying (8-PSK) modulations for 2,3 and 4 bits/s/Hz spectral efficiencies. It is shown via computer simulations and also supported by a theoretical error performance analysis that the proposed SM-TC schemes achieve significantly better error performance than the classical space-time trellis codes and coded V-BLAST systems at the same spectral efficiency, yet with reduced decoding complexity.