nils-herrmann/clean_scirepeval huggingface dataset:
The Three-Dimensional Bin Packing Problem

The problem addressed in this paper is that of orthogonally packing a given set of rectangular-shaped items into the minimum number of three-dimensional rectangular bins. The problem is strongly NP-hard and extremely diicult to solve in practice. Lower bounds are discussed, and it is proved that the asymptotic worst-case performance ratio of the continuous lower bound is 1 8. An exact algorithm for lling a single bin is developed, leading to the deenition of an exact branch-and-bound algorithm for the three-dimensional bin packing problem, which also incorporates original approximation algorithms. Extensive computational results, involving instances with up to 90 items, are presented: it is shown that many instances can be solved to optimality within a reasonable time limit.
A 22-Week-Old Fetus with Nager Syndrome and Congenital Diaphragmatic Hernia due to a Novel SF3B4 Mutation.

Nager syndrome, or acrofacial dysostosis type 1 (AFD1), is a rare multiple malformation syndrome characterized by hypoplasia of first and second branchial arches derivatives and appendicular anomalies with variable involvement of the radial/axial ray. In 2012, AFD1 has been associated with dominant mutations in SF3B4. We report a 22-week-old fetus with AFD1 associated with diaphragmatic hernia due to a previously unreported SF3B4 mutation (c.35-2A>G). Defective diaphragmatic development is a rare manifestation in AFD1 as it is described in only 2 previous cases, with molecular confirmation in 1 of them. Our molecular finding adds a novel pathogenic splicing variant to the SF3B4 mutational spectrum and contributes to defining its prenatal/fetal phenotype.
Analytics on Fast Data: Main-Memory Database Systems versus Modern Streaming Systems

Today’s streaming applications demand increasingly high event throughput rates and are often subject to strict latency constraints. To allow for more complex workloads, such as window-based aggregations, streaming systems need to support stateful event processing. This introduces new challenges for streaming engines as the state needs to be maintained in a consistent and durable manner and simultaneously accessed by complex queries for real-time analytics. Modern streaming systems, such as Apache Flink, do not allow for efficiently exposing the state to analytical queries. Thus, data engineers are forced to keep the state in external data stores, which significantly increases the latencies until events are visible to analytical queries. Proprietary solutions have been created to meet data freshness constraints. These solutions are expensive, error-prone, and difficult to maintain. Main-memory database systems, such as HyPer, achieve extremely low query response times while maintaining high update rates, which makes them well-suited for analytical streaming workloads. In this paper, we identify potential extensions to database systems to match the performance and usability of streaming systems.
Dynamic dexterity of a planar 2-DOF parallel manipulator in a hybrid machine tool

Robotica / Volume 26 / Issue 01 / January 2008,  2007 Link to this article: http://journals.cambridge.org/abstract_S0263574707003621 How to cite this article: Jun Wu, Jinsong Wang, Tiemin Li, Liping Wang and Liwen Guan (2008). Dynamic dexterity of a planar 2-DOF parallel manipulator in a hybrid machine tool. Robotica, 26, pp 93-98  Click here
Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach

Online consumer reviews (OCR) have helped consumers to know about the strengths and weaknesses of different products and find the ones that best suit their needs. This research investigates the predictors of readership and helpfulness of OCR using a sentiment mining approach. Our findings show that reviews with higher levels of positive sentiment in the title receive more readerships. Sentimental reviews with neutral polarity in the text are also perceived to be more helpful. The length and longevity of a review positively influence both its readership and helpfulness. Our findings suggest that the current methods used for sorting OCR may bias both their readership and helpfulness. This study can be used by online vendors to develop scalable automated systems for sorting and classification of OCR which will benefit both vendors and consumers.
Bilateral Transverse Facial Cleft as an Isolated and Asyndromic Deformity

Congenital macrostomia or transverse facial cleft is a rare congenital craniofacial anomaly, which affects the esthetics and functions of oral cavity. It is usually associated with deformities of other structures developed from the first and second branchial arches. Bilateral transverse cleft, occurring alone is uncommon. Since the deformity is rare, its treatment has not been commonly described in the literature. We report a case of congenital bilateral macrostomia as an isolated, asyndromic deformity to add one more case in the literature and surgical technique has been discussed here.
Solving document clustering problem through meta heuristic algorithm: black hole

The paper proposed a soft computing approach to solve document clustering problem. Document clustering is a specialized clustering problem in which textual documents autonomously segregated to a number of identifiable, subject homogenous and smaller sub-collections (also called clusters). Identifying implicit textual patterns within the documents is a challenging aspect as there can be thousands of such textual features. Partition clustering algorithm like k-means is mainly used for this problem. There are several drawbacks in k-means algorithm such as (i) initial seeds dependency, and (ii) it traps into local optimal solution. Although every k-means solution may contain some good partial arrangements for clustering. Meta-heuristic algorithm like Black Hole (BH) uses certain trade-off of randomization and local search for finding the optimal and near optimal solution. Our motivation comes from the fact that meta-heuristic optimization can quickly produce a global optimal solution using random k-means initial solution. The contributions from this research are (i) an implementation of black hole algorithm using k-mean as embedding (ii) The phenomena of global search and local search optimization are used as parameters adjustments. A series of experiments are performed with our proposed method on standard text mining datasetslike: (i) NEWS20, (ii) Reuters and (iii) WebKB and results are evaluated on Purity and Silhouette Index. In comparison the proposed method outperforms the basic k-means, GA with k-means embedding and quickly converges to global or near global optimal solution.
UWB microstrip antenna based on circular patch topology with stepped blocks (wing)

This paper presents the design of Ultra Wideband (UWB) microstrip antenna consisting of a circular monopole patch antenna with 3 block stepped (wing). The antenna design is an improvement from previous research and it is simulated using CST Microwave Studio software. This antenna was designed on Rogers 5880 printed circuit board (PCB) with overall size of 26 × 40 × 0.787 mm3 and dielectric substrate, εr = 2.2. The performance of the designed antenna was analyzed in term of bandwidth, gain, return loss, radiation pattern, and verified through actual measurement of the fabricated antenna. 10 dB return loss bandwidth from 3.37 GHz to 10.44 GHz based on 50 ohm characteristic impedance for the transmission line model was obtained.
Merging trust in collaborative filtering to alleviate data sparsity and cold start

Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it suffers from several inherent issues such as data sparsity and cold start. To address these problems, we propose a novel method called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Specifically, ratings of a user's trusted neighbors are merged to complement and represent the preferences of the user and to find other users with similar preferences (i.e., similar users). In addition, the quality of merged ratings is measured by the confidence considering the number of ratings and the ratio of conflicts between positive and negative opinions. Further, the rating confidence is incorporated into the computation of user similarity. The prediction for a given item is generated by aggregating the ratings of similar users. Experimental results based on three real-world data sets demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage. The emergence of Web 2.0 applications has greatly changed users' styles of online activities from searching and browsing to interacting and sharing [6,40]. The available choices grow up exponentially, and make it challenge for users to find useful information which is well-known as the information overload problem. Recommender systems are designed and heavily used in modern e-commerce applications to cope with this problem, i.e., to provide users with high quality, personalized recommendations , and to help them find items (e.g., books, movies, news, music, etc.) of interest from a plethora of available choices. Collaborative filtering (CF) is one of the most well-known and commonly used techniques to generate recommendations [1,17]. The heuristic is that the items appreciated by those who have similar taste will also be in favor of by the active users (who desire recommendations). However, CF suffers from several inherent issues such as data sparsity and cold start. The former issue refers to the difficulty in finding sufficient and reliable similar users due to the fact that users in general only rate a small portion of items, while the latter refers to the dilemma that accurate recommendations are expected for the cold users who rate only a few items and thus whose preferences are hard to be inferred. To resolve these issues and model …
Cardiac Masses on Cardiac CT: A Review

Cardiac masses are rare entities that can be broadly categorized as either neoplastic or non-neoplastic. Neoplastic masses include benign and malignant tumors. In the heart, metastatic tumors are more common than primary malignant tumors. Whether incidentally found or diagnosed as a result of patients' symptoms, cardiac masses can be identified and further characterized by a range of cardiovascular imaging options. While echocardiography remains the first-line imaging modality, cardiac computed tomography (cardiac CT) has become an increasingly utilized modality for the assessment of cardiac masses, especially when other imaging modalities are non-diagnostic or contraindicated. With high isotropic spatial and temporal resolution, fast acquisition times, and multiplanar image reconstruction capabilities, cardiac CT offers an alternative to cardiovascular magnetic resonance imaging in many patients. Additionally, cardiac masses may be incidentally discovered during cardiac CT for other reasons, requiring imagers to understand the unique features of a diverse range of cardiac masses. Herein, we define the characteristic imaging features of commonly encountered and selected cardiac masses and define the role of cardiac CT among noninvasive imaging options.
Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder

Bipolar Disorder is a severe form of mental illness. It is characterized by alternated episodes of mania and depression, and it is treated typically with a combination of pharmacotherapy and psychotherapy. Recognizing early warning signs of upcoming phases of mania or depression would be of great help for a personalized medical treatment. Unfortunately, this is a difficult task to be performed for both patient and doctors. In this paper we present the MONARCA wearable system, which is meant for recognizing early warning signs and predict maniac or depressive episodes. The system is a smartphone-centred and minimally invasive wearable sensors network that is being developing in the framework of the MONARCA European project.
Effect of mold treatment by solvent on PDMS molding into nanoholes

Polydimethylsiloxane (PDMS) is the most popular and versatile material for soft lithography due to its flexibility and easy fabrication by molding process. However, for nanoscale patterns, it is challenging to fill uncured PDMS into the holes or trenches on the master mold that is coated with a silane anti-adhesion layer needed for clean demolding. PDMS filling was previously found to be facilitated by diluting it with toluene or hexane, which was attributed to the great reduction of viscosity for diluted PDMS. Here, we suggest that the reason behind the improved filling for diluted PDMS is that the diluent solvent increases in situ the surface energy of the silane-treated mold and thus the wetting of PDMS to the mold surface. We treated the master mold surface (that was already coated with a silane anti-adhesion monolayer) with toluene or hexane, and found that the filling by undiluted PMDS into the nanoscale holes on the master mold was improved despite the high viscosity of the undiluted PDMS. A simple estimation based on capillary filing into a channel also gives a filling time on the millisecond scale, which implies that the viscosity of PMDS should not be the limiting factor. We achieved a hole filling down to sub-200-nm diameter that is smaller than those of the previous studies using regular Sylgard PDMS (not hard PDMS, Dow Corning Corporation, Midland, MI, USA). However, we are not able to explain using a simple argument based on wetting property why smaller, e.g., sub-100-nm holes, cannot be filled, for which we suggested a few possible factors for its explanation.
Integration of Angle of Arrival Information for Multimodal Sensor Network Localization using Semidefinite Programming

The problem of position estimation in sensor networks using a combination of distance and angle information as well as pure angle information is discussed. For this purpose, a semidefinite programming relaxation based method that has been demonstrated on pure distance information is extended to solve the problem. Practical considerations such as the effect of noise and computational effort are also addressed. In particular, a random constraint selection method to minimize the number of constraints in the problem formulation is described. The performance evaluation of the technique with regard to estimation accuracy and computation time is also presented by the means of extensive simulations.
Promising directions in active vision

Active vision systems have mechanisms that can actively control camera parameters such as position, orientation, focus, zoom, aperture, and vergence (in a two-camera system) in response to the requirements of the task and external stimuli. They may also have features such as spatially variant (foveal) sensors. More broadly, active vision encompassesattention, selective sensing in space, resolution, and time, whether it is achieved by modifying physical camera parameters or the way data is processed after leaving the camera. In the active-vision paradigm, the basic components of the visual system are visual behaviors tightly integrated with the actions they support; these behaviors may not require elaborate categorical representations of the 3-D world. Because the cost of generating and updating a complete, detailed model of most environments is too high, this approach to vision is vital for achieving robust, real-time perception in the real world. In addition, active control of imaging parameters has been shown to simplify scene interpretation by eliminating the ambiguity present in single images. Important research areas in active vision include attention, foveal sensing, gaze control, eye-hand coordination, and integration with robot architectures.
Canonical Tensor Decomposition for Knowledge Base Completion

The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution; however, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their competitors. In this work, we attempt to understand the limits of CP for knowledge base completion. First, we motivate and test a novel regularizer, based on tensor nuclear p-norms. Then, we present a reformulation of the problem that makes it invariant to arbitrary choices in the inclusion of predicates or their reciprocals in the dataset. These two methods combined allow us to beat the current state of the art on several datasets with a CP decomposition, and obtain even better results using the more advanced ComplEx model.
Improving the Performance Efficiency of an IDS by Exploiting Temporal Locality in Network Traffic

Network traffic has traditionally exhibited temporal locality in the header field of packets. Such locality is intuitive and is a consequence of the semantics of network protocols. However, in contrast, the locality in the packet payload has not been studied in significant detail. In this work we study temporal locality in the packet payload. Temporal locality can also be viewed as redundancy, and we observe significant redundancy in the packet payload. We investigate mechanisms to exploit it in a networking application. We choose Intrusion Detection Systems (IDS) as a case study. An IDS like the popular Snort operates by scanning packet payload for known attack strings. It first builds a Finite State Machine (FSM) from a database of attack strings, and traverses this FSM using bytes from the packet payload. So temporal locality in network traffic provides us an opportunity to accelerate this FSM traversal. Our mechanism dynamically identifies redundant bytes in the packet and skips their redundant FSM traversal. We further parallelize our mechanism by performing the redundancy identification concurrently with stages of Snort packet processing. IDS are commonly deployed in commodity processors, and we evaluate our mechanism on an Intel Core i3. Our performance study indicates that the length of the redundant chunk is a key factor in performance. We also observe important performance benefits in deploying our redundancy-aware mechanism in the Snort IDS[32].
Redefining Chronic Viral Infection

Viruses that cause chronic infection constitute a stable but little-recognized part of our metagenome: our virome. Ongoing immune responses hold these chronic viruses at bay while avoiding immunopathologic damage to persistently infected tissues. The immunologic imprint generated by these responses to our virome defines the normal immune system. The resulting dynamic but metastable equilibrium between the virome and the host can be dangerous, benign, or even symbiotic. These concepts require that we reformulate how we assign etiologies for diseases, especially those with a chronic inflammatory component, as well as how we design and interpret genome-wide association studies, and how we vaccinate to limit or control our virome.
ADL recognition based on the combination of RFID and accelerometer sensing

The manual assessment of activities of daily living (ADLs) is a fundamental problem in elderly care. The use of miniature sensors placed in the environment or worn by a person has great potential in effective and unobtrusive long term monitoring and recognition of ADLs. This paper presents an effective and unobtrusive activity recognition system based on the combination of the data from two different types of sensors: RFID tag readers and accelerometers. We evaluate our algorithms on non-scripted datasets of 10 housekeeping activities performed by 12 subjects. The experimental results show that recognition accuracy can be significantly improved by fusing the two different types of sensors. We analyze different acceleration features and algorithms, and based on tag detections we suggest the best tagspsila placements and the key objects to be tagged for each activity.
Person Organization Fit, Organizational Commitment and Knowledge Sharing Attitude–an Analytical Study

The purpose of this study is to analyze the relationship among Person Organization Fit (POF), Organizational Commitment (OC) and Knowledge Sharing Attitude (KSA). The paper develops a conceptual frame based on a theory and literature review. A quantitative approach has been used to measure the level of POF and OC as well as to explore the relationship of these variables with KSA & with each other by using a sample of 315 academic managers of public sector institutions of higher education. POF has a positive relationship with OC and KSA. A positive relationship also exists between OC and KSA. It would be an effective contribution in the existing body of knowledge. Managers and other stakeholders may be helped to recognize the significance of POF, OC and KSA as well as their relationship with each other for ensuring selection of employee’s best fitted in the organization and for creating and maintaining a conducive environment for improving organizational commitment and knowledge sharing of the employees which will ultimately result in enhanced efficacy and effectiveness of the organization.
A distributed IDS architecture model for Smart Home systems

The common use of smart devices encourages potential attackers to violate privacy. Sometimes taking control of one device allows the attacker to obtain secret data (such as password for home WiFi network) or tools to carry out DoS attack, and this, despite the limited resources of such devices. One of the solutions for gaining users’ confidence is to assign responsibility for detecting attacks to the service provider, particularly Internet Service Provider (ISP). It is possible, since ISP often provides also the Home Gateway (HG)—device that has multiple roles: residential router, entertainment center, and home’s “command and control” center which allows to manage the Smart Home entities. The ISP may extend this set of functionalities by implementing an intrusion detection software in HG provisioned to their customers. In this article we propose an Intrusion Detection System (IDS) distributed between devices residing at user’s and ISP’s premises. The Home Gateway IDS and the ISP’s IDS constitute together a distributed structure which allows spreading computations related to attacks against Smart Home ecosystem. On the other hand, it also leverages the operator’s knowledge of security incidents across the customer premises. This distributed structure is supported by the ISP’s expert system that helps to detect distributed attacks i.e., using botnets.
Slideshow: Gesture-aware PPT presentation

Traditional keyboard and mouse based presentation prevents lecturers from interacting with the audiences freely and closely. In this paper, we propose a gesture-aware presentation tool named SlideShow to liberate lecturers from physical space constraints and make human-computer interaction more natural and convenient. In our system, gesture data is obtained by a handle controller with 3-axis accelerometer and gyro and transmitted to host-side through bluetooth, then we use Bayesian change point detection to segment continuous gesture series and HMM to recognize the gesture. In consequence Slideshow could carry out the corresponding operations on PowerPoint(PPT) to make a presentation, and operation states can be switched automatically and intelligently during the presentation. Both the experimental and testing results show our approach is practical, useful and convenient.