THE INFLUENCE OF SOCIAL PRESENCE ON EVALUATING PERSONALIZED RECOMMENDER SYSTEMS

Providing recommendations is acknowledged as an important feature of a business-to-consumer online storefront. Although many studies have been conducted the algorithms and operational procedures relating to personalized recommender systems, empirical evidence demonstrating relationships between social presence and two important outcomes of evaluating recommender systems, reuse intention and trust, remains lacking. To test the existence of a causal link between social presence and reuse intention, and the mediating role of trust between these two variables, this study conducted experiments varying the levels of social presence while providing personalized recommendations to users based on their explicit preferences. This study also compared these effects in two different product contexts: hedonic and utilitarian products. Interactions of social presence and customer reviews were also investigated in these experiments. The results show that higher social presence increases both reuse intention and trust in recommender systems. In addition, the influence of social presence on reuse intention in the context of recommending utilitarian products is less than that in the context of recommending hedonic products

COMPARATIVE ANALYSIS OF YANG HAK-SEON VAULT AND TSUKAHARA 1260? VAULT IN GYMNASTICS

The study was a single-subject study on the top-elite vaulter in the world. This study was purposed to compare kinematic differences between Yang Hak-Seon vault (Yang-1) and Tsukahara 1260? vault (Yang-2) performed by Yang Hak-Seon. Fourteen high-speed cameras were used to capture a whole body segment motion of Yang-2 vault during the practice session. Yang-1 vault showed faster CM vertical velocity until the vault table takeoff and faster CM horizontal velocity prior to the vault table touchdown. However, the trunk rotation angle and its angular velocity of Yang-2 vault exceeded Yang-1 vault significantly. This might be due to a half turn off the springboard onto the vault table of Yang-2 vault, which resulted in larger initial angular momentum at the vault table touchdown and further increase in angular velocity during the vault table contact

Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics

Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics

Utilizing ECG Waveform Features as New Biometric Authentication Method

In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%

Measurement of beta-amyloid peptides in specific cells using a photo thin-film transistor

The existence of beta-amyloid [Aβ] peptides in the brain has been regarded as the most archetypal biomarker of Alzheimer's disease [AD]. Recently, an early clinical diagnosis has been considered a great importance in identifying people who are at high risk of AD. However, no microscale electronic sensing devices for the detection of Aβ peptides have been developed yet. In this study, we propose an effective method to evaluate a small quantity of Aβ peptides labeled with fluorescein isothiocyanate [FITC] using a photosensitive field-effect transistor [p-FET] with an on-chip single-layer optical filter. To accurately evaluate the quantity of Aβ peptides within the cells cultured on the p-FET device, we measured the photocurrents which resulted from the FITC-conjugated Aβ peptides expressed from the cells and measured the number of photons of the fluorochrome in the cells using a photomultiplier tube. Thus, we evaluated the correlation between the generated photocurrents and the number of emitted photons. We also evaluated the correlation between the number of emitted photons and the amount of FITC by measuring the FITC volume using AFM. Finally, we estimated the quantity of Aβ peptides of the cells placed on the p-FET sensing area on the basis of the binding ratio between FITC molecules and Aβ peptides

Primary Uterine Lymphoma: A Case Report

Primary lymphoma of the uterus is a rare disease, the reported characteristic MR imaging findings being homogeneous intermediate signal intensity of the indistinct mass on T1- and T2-weighted images, and the preservation of endometrial lining and uterine architecture. We report a case of primary uterine lymphoma which showed tumoral necrosis, endometrial disruption and diffuse anterior vaginal wall involvement