nils-herrmann/clean_scirepeval huggingface dataset:
Security testing for Android mHealth apps

Mobile health (mHealth) apps are an ideal tool for monitoring and tracking long-term health conditions; they are becoming incredibly popular despite posing risks to personal data privacy and security. In this paper, we propose a testing method for Android mHealth apps which is designed using a threat analysis, considering possible attack scenarios and vulnerabilities specific to the domain. To demonstrate the method, we have applied it to apps for managing hypertension and diabetes, discovering a number of serious vulnerabilities in the most popular applications. Here we summarise the results of that case study, and discuss the experience of using a testing method dedicated to the domain, rather than out-of-the-box Android security testing methods. We hope that details presented here will help design further, more automated, mHealth security testing tools and methods.
Reliable and Privacy-Preserving Selective Data Aggregation for Fog-Based IoT

Internet of Things (IoT) is reshaping our daily lives by bridging the gaps between physical and digital world. To enable ubiquitous sensing, seamless connection and real-time processing for IoT applications, fog computing is considered as a key component in a heterogeneous IoT architecture, which deploys storage and computing resources to network edges. However, the fog-based IoT architecture can lead to various security and privacy risks, such as compromised fog nodes that may impede developments of IoT by attacking the data collection and gathering period. In this paper, we propose a novel privacy-preserving and reliable scheme for the fog-based IoT to address the data privacy and reliability challenges of the selective data aggregation service. Specifically, homomorphic proxy re-encryption and proxy re-authenticator techniques are respectively utilized to deal with the data privacy and reliability issues of the service, which supports data aggregation over selective data types for any type-driven applications. We define a new threat model to formalize the non-collusive and collusive attacks of compromised fog nodes, and it is demonstrated that the proposed scheme can prevent both non-collusive and collusive attacks in our model. In addition, performance evaluations show the efficiency of the scheme in terms of computational costs and communication overheads.
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning

Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from small sets of example interactions. We describe a learning approach as a step in this direction, where we teach a humanoid robot how to play the game of noughts and crosses. Given that multiple multimodal skills can be trained to play this game, we focus our attention to training the robot to perceive the game, and to interact in this game. Our multimodal deep reinforcement learning agent perceives multimodal features and exhibits verbal and non-verbal actions while playing. Experimental results using simulations show that the robot can learn to win or draw up to 98% of the games. A pilot test of the proposed multimodal system for the targeted game—integrating speech, vision and gestures—reports that reasonable and fluent interactions can be achieved using the proposed approach.
Complementary Phase Power Divider Feed for Dipole Antenna Specific to GSM 900 Base Station Applications

Power Divider (PD) design intended for feeding dipole antenna meant for Global System for Mobile Communications (GSM) 900 applications with an antenna height of 22 mm, operating in the frequency range of 880~960 MHz is presented herein. The PD provides 3 dB power division along with complementary phase at its output. The out of phase division of power divider is obtained by utilizing the concept of defected ground structures. A slot line accompanied by T-junction makes up the defected ground region, while coupled microstrip lines form the feed positions. The simulated and the measured results are in good coherence. PD shows good return loss and low insertion loss. It is observed that the dual polarized base station antenna has VSWR below 2 in the required frequency range, 10 dB gain and port to port isolation is less than 22 dB. The antenna is dual polarized and a + or - 45 degrees polarization is maintained.
Semantic Segmentation With Global Encoding and Dilated Decoder in Street Scenes

Semantic segmentation as a pixel-wise segmentation task provides rich object information, and it has been widely applied in many fields ranging from autonomous driving to medical image analysis. There are two main challenges on existing approaches: the first one is the obfuscation between objects resulted from the prediction of the network and the second one is the lack of localization accuracy. Hence, to tackle these challenges, we proposed global encoding module (GEModule) and dilated decoder module (DDModule). Specifically, the GEModule that integrated traditional dictionary learning and global semantic context information is to select discriminative features and improve performance. DDModule that combined dilated convolution and dense connection is used to decoder module and to refine the prediction results. We evaluated our proposed architecture on two public benchmarks, Cityscapes and CamVid data set. We conducted a series of ablation studies to exploit the effectiveness of each module, and our approach has achieved an intersection-over-union scores of 71.3% on the Cityscapes data set and 60.4% on the CamVid data set.
Knowledge Management as a Catalyst for Innovation within Organizations: A Qualitative Study

The need for organizations to innovate and furthermore to ceaselessly innovate is stressed throughout the modern management literature on innovation. This need comes from increasing competition and customer demands and new market areas. Closely linked, but not synonymous, with innovation is the body of knowledge referred to collectively as knowledge management. Within this discourse knowledge is considered as a potential key competitive advantage, by helping to increase innovation within the organization. This paper focuses on the role of knowledge management in sustaining and enhancing innovation in organizations. In particular the paper seeks to establish a knowledge management model within which the principles of innovation can be incorporated. First, there is a brief review of the innovation and knowledge management literature and their respective synergies. From this literature a possible knowledge management model which incorporates innovation is suggested. Second, a research study is discussed which seeks to further examine and develop the model using an inductive grounded theory approach. The study involved socially constructed workshops representing 25 organizations, each of which constructed meanings in regard to innovation and the key areas of knowledge management as outlined in the model. Overall it was found that effective systematic knowledge management can incorporate innovation drivers in key areas which will result in both increased business and employee bene®ts.
AUTOMATIC TUNING OF PROPORTIONAL- INTEGRAL-DERIVATIVE (PID) CONTROLLER USING PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM

The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. ZieglerNichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the ZieglerNichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
A Low-Power Broad-Bandwidth Noise Cancellation VLSI Circuit Design for In-Ear Headphones

Conventional active noise cancelling (ANC) headphones often perform well in reducing the low-frequency noise and isolating the high-frequency noise by earmuffs passively. The existing ANC systems often use high-speed digital signal processors to cancel out disturbing noise, which results in high power consumption for a commercial ANC headphone. The contribution of this paper can be classified into: 1) proper filter length selection; 2) low-power storage mechanism for convolution operation; and 3) high-throughput pipelining architecture. With these novel techniques, we develop an area-/power-efficient ANC circuit by using the TSMC 90-nm CMOS technology for in-ear headphone applications. The proposed feedforward filtered-x least mean square ANC circuit design provides the features of using lower operating frequency and consuming much less power that facilitate better performance than the conventional ANC headphones. To verify the effectiveness of the proposed design, a series of physical measurements is executed in an anechoic chamber. Measurement results show that the proposed high-performance/low-power circuit design can reduce disturbing noise of various frequency bands very well, and outperforms the existing works. The proposed design can attenuate 15 dB for broadband pink noise between 50 and 1500 Hz when operated at 20-MHz clock frequency at the costs of 84.2 k gates and power consumption of 6.59 mW only. Compared with the existing designs, the proposed work achieves higher noise cancellation performance in terms of 3 dB further and saves 97% power consumption.
Disentangling the many layers of eukaryotic transcriptional regulation.

Regulation of gene expression in eukaryotes is an extremely complex process. In this review, we break down several critical steps, emphasizing new data and techniques that have expanded current gene regulatory models. We begin at the level of DNA sequence where cis-regulatory modules (CRMs) provide important regulatory information in the form of transcription factor (TF) binding sites. In this respect, CRMs function as instructional platforms for the assembly of gene regulatory complexes. We discuss multiple mechanisms controlling complex assembly, including cooperative DNA binding, combinatorial codes, and CRM architecture. The second section of this review places CRM assembly in the context of nucleosomes and condensed chromatin. We discuss how DNA accessibility and histone modifications contribute to TF function. Lastly, new advances in chromosomal mapping techniques have provided increased understanding of intra- and interchromosomal interactions. We discuss how these topological maps influence gene regulatory models.
Substrate Integrated Waveguide Leaky-Wave Antenna With H-Shaped Slots

A new leaky-wave antenna based on substrate integrated waveguide (SIW) with H-shaped slots is proposed and investigated. The SIW with H-shaped slots is analyzed as a rectangular waveguide with H-shaped slots. Using an aperture magnetic field integral equation (MFIE), it is found that the SIW with H-shaped slots supports a leaky waveguide mode, a surface-wave mode and a proper waveguide mode depending on frequency. The radiation property is also evaluated using the MFIE. The leaky-wave antenna based on the SIW with H-shaped slots has a narrow beam that scans from broadside to endfire with frequency. The antenna produces an elliptical polarization, including circular polarization. Measured results are consistent with the simulation from HFSS and the theoretical analysis from the MFIE.
Small Director Array for Low-Profile Smart Antennas Achieving Higher Gain

A small director array (SDA) is an antenna gain enhancing section of the low-profile smart antenna which can be used as a fixed array or reconfigurable array using switched parasitic elements. Gain improvements up to +10 dBi are achieved in the SDA through a Yagi-Uda configuration using parasitic elements with a large diameter. The array height is reduced by 50% comparing with the standard Yagi-Uda antenna. The reconfigurable SDA used an electronically steerable switched parasitic arrangement so that the beam can be steered from 0° to 360° in the horizontal plane. The height of the reconfigurable SDA is 0.2 λ. The measurements proved that the reconfigurable SDA can increase the antenna gain by 3 dB. The front to back ratio showed significant improvement. The proposed SDA also had the advantages of low cost and low power consumption, thus promising for applications in small satellites, unmanned aerial vehicles and mobile terminals.
The Shadow Nemesis: Inference Attacks on Efficiently Deployable, Efficiently Searchable Encryption

Encrypting Internet communications has been the subject of renewed focus in recent years. In order to add end-to-end encryption to legacy applications without losing the convenience of full-text search, ShadowCrypt and Mimesis Aegis use a new cryptographic technique called "efficiently deployable efficiently searchable encryption" (EDESE) that allows a standard full-text search system to perform searches on encrypted data. Compared to other recent techniques for searching on encrypted data, EDESE schemes leak a great deal of statistical information about the encrypted messages and the keywords they contain. Until now, the practical impact of this leakage has been difficult to quantify.
 In this paper, we show that the adversary's task of matching plaintext keywords to the opaque cryptographic identifiers used in EDESE can be reduced to the well-known combinatorial optimization problem of weighted graph matching (WGM). Using real email and chat data, we show how off-the-shelf WGM solvers can be used to accurately and efficiently recover hundreds of the most common plaintext keywords from a set of EDESE-encrypted messages. We show how to recover the tags from Bloom filters so that the WGM solver can be used with the set of encrypted messages that utilizes a Bloom filter to encode its search tags. We also show that the attack can be mitigated by carefully configuring Bloom filter parameters.
Konark: A RFID based System for Enhancing In-store Shopping Experience

In this paper, we introduce a Radio Frequency IDentification (RFID) based smart shopping system, KONARK, which helps users to checkout items faster and to track purchases in real-time. In parallel, our solution also provides the shopping mall owner with information about user interest on particular items. The central component of KONARK system is a customized shopping cart having a RFID reader which reads RFID tagged items. To provide check-out facility, our system detects in-cart items with almost 100% accuracy within 60s delay by exploiting the fact that the physical level information (RSSI, phase, doppler, read rate etc.) of in-cart RFID tags are different than outside tags. KONARK also detects user interest with 100% accuracy by exploiting the change in physical level parameters of RFID tag on the object user interacted with. In general, KONARK has been shown to perform with reasonably high accuracy in different mobility speeds in a mock-up of a shopping mall isle.
A 5-year retrospective study of survival of zirconia single crowns fitted in a private clinical setting.

The aim of this report was to evaluate the 5-year clinical performance and survival of zirconia (NobelProcera™) single crowns.



All patients treated with porcelain-veneered zirconia single crowns in a private practice during the period October 2004 to November 2005 were included. The records were scrutinized for clinical data. Information was available for 162 patients and 205 crowns.



Most crowns (78%) were placed on premolars and molars. Out of the 143 crowns that were followed for 5 years, 126 (88%) did not have any complications. Of those with complications, the most common were: extraction of abutment tooth (7; 3%), loss of retention (15; 7%), need of endodontic treatment (9; 4%) and porcelain veneer fracture (6; 3%). No zirconia cores fractured. In total 19 restorations (9%) were recorded as failures: abutment tooth extraction (7), remake of crown due to lost retention (6), veneer fracture (4), persistent pain (1) and caries (1). The 5-year cumulative survival rate (CSR) was 88.8%.



According to the present 5-year results zirconia crowns (NobelProcera™) are a promising prosthodontic alternative also in the premolar and molar regions. Out of the 143 crowns followed for 5 years, 126 (88%) did not have any complications. However, 9% of the restorations were judged as failures. Further studies are necessary to evaluate the long-term success.
Improving Handwritten Arabic Character Recognition by Modeling Human Handwriting Distortions

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and the unavailability of large public databases of handwritten characters and words. The use of synthetic data for training and testing handwritten character recognition systems is one of the possible solutions to provide several variations for these characters and to overcome the lack of large databases. While this can be using arbitrary distortions, such as image noise and randomized affine transformations, such distortions are not realistic. In this work, we model real distortions in handwriting using real handwritten Arabic character examples and then use these distortion models to synthesize handwritten examples that are more realistic. We show that the use of our proposed approach leads to significant improvements across different machine-learning classification algorithms.
An adaptive parameter space-filling algorithm for highly interactive cluster exploration

For a user to perceive continuous interactive response time in a visualization tool, the rule of thumb is that it must process, deliver, and display rendered results for any given interaction in under 100 milliseconds. In many visualization systems, successive interactions trigger independent queries and caching of results. Consequently, computationally expensive queries like multidimensional clustering cannot keep up with rapid sequences of interactions, precluding visual benefits such as motion parallax. In this paper, we describe a heuristic prefetching technique to improve the interactive response time of KMeans clustering in dynamic query visualizations of multidimensional data. We address the tradeoff between high interaction and intense query computation by observing how related interactions on overlapping data subsets produce similar clustering results, and characterizing these similarities within a parameter space of interaction. We focus on the two-dimensional parameter space defined by the minimum and maximum values of a time range manipulated by dragging and stretching a one-dimensional filtering lens over a plot of time series data. Using calculation of nearest neighbors of interaction points in parameter space, we reuse partial query results from prior interaction sequences to calculate both an immediate best-effort clustering result and to schedule calculation of an exact result. The method adapts to user interaction patterns in the parameter space by reprioritizing the interaction neighbors of visited points in the parameter space. A performance study on Mesonet meteorological data demonstrates that the method is a significant improvement over the baseline scheme in which interaction triggers on-demand, exact-range clustering with LRU caching. We also present initial evidence that approximate, temporary clustering results are sufficiently accurate (compared to exact results) to convey useful cluster structure during rapid and protracted interaction.
“ Affetto ” : towards a design of robots who can physically interact with people , which biases the perception of affinity ( beyond “ uncanny ” ) *

This article presents the upper-torso design issue of Affeto who can physically interact with humans, which biases the perception of affinity beyond the uncanny valley effect. First, we review the effect and hypothesize that the experience of physical interaction with Affetto decreases the effect. Then, the reality of physical existence is argued with existing platforms. Next, the design concept and a very preliminary experiment are shown. Finally, future issues are given. I. THE UNCANNY VALLEY REVISITED The term “Uncanny” is a translation of Freud’s term “Der Unheimliche” and applied to a phenomenon noted by Masahiro Mori who mentioned that the presence of movement steepens the slopes of the uncanny valley . Several studies on this effect can be summarised as follows1. 1) Multimodal impressions such as visual appearance, body motion, sounds (speech and others), and tactile sensation should be congruent to decrease the valley steepness. 2) Antipathetic expressions may exaggerate the valley effect. The current technologies enable us to minimize the gap caused by mismatch among cross-modal factors. Therefore, the valley effect is expected to be reduced gradually. For example, facial expressions and tactile sensations of Affetto  are realistic and congruent due to baby-like face skin mask of urethane elastomer gel . Generated facial expressions almost conquered the uncanny valley. Further, baby-like facial expressions may contribute to the reduction of the valley effect due to 2). In addition to these, we suppose that the motor experience of physical interactions with robots biases the perception of affinity as motor experiences biases the perception of movements . To verify this hypothesis, Affetto needs its body which realizes physical interactions naturally. The rest of this article is organized as follows. The next section argues about the reality of physical existence with existing platforms. Then, the design concept and a very preliminary experiment are shown, and the future issues are given.
Management of a case of arhinia.

Arhinia is a rare condition characterised by the congenital absence of nasal structures, with different patterns of presentation, and often associated with other craniofacial or somatic anomalies. To date, about 30 surviving cases have been reported. We report the case of a female patient aged 6 years, who underwent internal and external nose reconstruction using a staged procedure: a nasal airway was obtained through maxillary osteotomy and ostectomy, and lined with a local skin flap and split-thickness skin grafts; then the external nose was reconstructed with an expanded frontal flap, armed with an autogenous rib framework.